Trials for Cerebellar Ataxias: From Cellular Models to Human Therapies 3031243447, 9783031243448

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Contemporary Clinical Neuroscience

Bing-wen Soong Mario Manto Alexis Brice Stefan M. Pulst   Editors

Trials for Cerebellar Ataxias From Cellular Models to Human Therapies

Contemporary Clinical Neuroscience Series Editor Mario Manto, Division of Neurosciences, Department of Neurology, CHU-Charleroi, Charleroi, Belgium, University of Mons, Mons, Belgium, Charleroi, Belgium

Contemporary Clinical Neurosciences bridges the gap between bench research in the neurosciences and clinical neurology work by offering translational research on all aspects of the human brain and behavior with a special emphasis on the understanding, treatment, and eradication of diseases of the human nervous system. These novel, state-of-the-art research volumes present a wide array of preclinical and clinical research programs to a wide spectrum of readers representing the diversity of neuroscience as a discipline. The book series considers proposals from leading scientists and clinicians. The main audiences are basic neuroscientists (neurobiologists, neurochemists, geneticians, experts in behavioral studies, neurophysiologists, neuroanatomists), clinicians (including neurologists, psychiatrists and specialists in neuroimaging) and trainees, graduate students, and PhD students. Volumes in the series provide in-depth books that focus on neuroimaging, ADHD (attention deficit hyperactivity disorder and other neuropsychiatric disorders, neurodegenerative diseases, G protein receptors, sleep disorders, addiction issues, cerebellar disorders, and neuroimmune diseases. The series aims to expand the topics at the frontiers between basic research and clinical applications. Each volume is available in both print and electronic form.

Bing-wen Soong  •  Mario Manto Alexis Brice  •  Stefan M. Pulst Editors

Trials for Cerebellar Ataxias From Cellular Models to Human Therapies

Editors Bing-wen Soong Professor of Neurology Shuang Ho Hospital, Taipei Medical University Taipei, (Republic of China), Taiwan Alexis Brice Paris Brain Institute (Institut du Cerveau – ICM), CNRS INSERM, Sorbonne Université, APHP Paris, France

Mario Manto Professor of Neuroanatomy University of Mons and CHU-Charleroi Charleroi, Belgium Stefan M. Pulst Department of Neurology Spencer Fox Eccles School of Medicine University of Utah Salt Lake City, UT, USA

ISSN 2627-535X     ISSN 2627-5341 (electronic) Contemporary Clinical Neuroscience ISBN 978-3-031-24344-8    ISBN 978-3-031-24345-5 (eBook) https://doi.org/10.1007/978-3-031-24345-5 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Despite the importance of the cerebellum in numerous brain functions, the scientific community still lacks effective treatments for most cerebellar ataxias, a group of disabling disorders affecting children, young adults, and the elderly. Encompassing practicing neurologists and physician scientists, the editors of the book share a common dream: getting the most treasurable holy grail by finding cures for our patients afflicted with ataxias. Many trials have intended to lead the way to end cerebellar ataxias. This book provides a link between molecular mechanisms, pathogenesis, and therapies of cerebellar ataxias. The book provides a comprehensive assessment of the pre-clinical and clinical trials dedicated to cerebellar ataxias in the last 25 years. For the past decades, many scientists have poured much blood, sweat, and tears working toward these ends. With their collective efforts, we believe that the light at the end of the tunnel is in sight. This is the first book fully dedicated to trials and therapies of cerebellar ataxias. The book comes at a time of major applications of genetic tools, neuroimaging, and other biomarkers, as well as innovative treatments. We are particularly grateful to the experts who contributed to the book by providing detailed and up-to-date chapters on these advances, to the referees, and to our patients and families contributing to clinical research. Taipei, (Republic of China), Taiwan Charleroi, Belgium Paris, France Salt Lake City, UT, USA

Bing-wen Soong Mario Manto Alexis Brice Stefan M. Pulst

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The List of Referees

The editors are deeply indebted to the following experts who served as referees. Despite their busy schedule, the reviewers helped scrutinize the manuscripts, provided constructive comments and recommendations. 1. Ashizawa, Tetsuo (Houston, USA) 2. Benarroch, Eduardo (Rochester, USA) 3. Bezprozvanny, Illy (Dallas, USA) 4. Blais, Mathieu (Québec, Canada) 5. Borroni, Barbara (Brescia, Italy) 6. Brice, Alexis (Paris, France) 7. Cendelin, Jan (Pilsen, the Czech Republic) 8. Chang, Kuo-Hsuan (Taipei, Taiwan (Republic of China)) 9. Cho, Jin Whan (Seoul, Korea) 10. Coarelli, Giulia (Paris, France) 11. Corben, Louise (Victoria, Australia) 12. de Gusmao, Claudio (Boston, USA) 13. Dupre, Nicolas (Québec, Canada) 14. Durr, Alexandra (Paris, France) 15. Ebner, Timothy (Minneapolis, USA) 16. Franca, Carina (São Paulo, Brazil) 17. Hadjivassiliou, Marios (Sheffield, UK) 18. Hartley, Helen (Liverpool, UK) 19. Hirai, Hirokazu (Gunma, Japan) 20. Ho, Kevin (Taipei, Taiwan (ROC)) 21. Ishikawa, Kinya (Tokyo, Japan) 22. Isope, Philippe (Strasbourg, France) 23. Kaczmarek, Leonard (New Haven, USA) 24. Klockgether, Thomas (Bonn, Germany) 25. Kuo, Hung-Chi (Taipei, Taiwan (ROC)) 26. Kuo, Sheng-Han (New York, USA) 27. La Spada, Albert (Irvine, USA) vii

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2 8. Lin, Chih-Chun (New York, USA) 29. Luiz Pedroso, José (São Paulo, Brazil) 30. Lynch, David (Philadelphia, USA) 31. Manto, Mario (Mons, Belgium) 32. Mariotti, Caterina (Milano, Italy) 33. Martinuzzi, Andrea (Conegliano, Italy) 34. Matilla-Dueñas, Antoni (Barcelona, Spain) 35. Matsushita, Matsuo (Tsukuba, Japan) 36. Milne, Sarah (Victoria, Australia) 37. Mitoma, Hiroshi (Tokyo, Japan) 38. Mizusawa, Hidehiro (Tokyo, Japan) 39. Olichney, John (Davis, USA) 40. Paulson, Henry (Ann Arbor, USA) 41. Pulst, Stefan (Salt Lake City, USA) 42. Ramirez-Zamora, Adolfo (Gainesville, USA) 43. Reetz, Kathrin (Aachen, Germany) 44. Rufini, Alessandra (Rome, Italy) 45. Schmahmann, Jeremy (Boston, USA) 46. Schmitz-Hübsch, Tanja (Berlin, Germany) 47. Scoles, Dan (Salt Lake City, USA) 48. Shaikh, Aasef (Cleveland, USA) 49. Soong, Bing-wen (New Taipei City, Taiwan (ROC)) 50. Stevanin, Giovanni (Paris, France) 51. Strupp, Michael (Munich, Germany) 52. Switonski, Pawel (Irvine, USA) 53. Todi, Sokol (Detroit, USA) 54. Tsuji, Shoji (Tokyo, Japan) 55. Ugawa, Yoshikazu (Fukushima, Japan) 56. Wang, Ling-Mei (Taipei, Taiwan (ROC)) 57. Xia Jiangyi (Davis, USA) 58. Yanagawa, Yuchio (Gunma, Japan) 59. Zanni, Ginevra (Rome, Italy) 60. Zesiewicz, Theresa (Tampa, USA)

The List of Referees

Contents

Part I Basic Science of Cerebellum and Ataxias  Functional Anatomy of the Cerebellum ��������������������������������������������������������    3 Izumi Sugihara, Yuanjun Luo, and Richard Nana Abankwah Owusu-Mensah Cerebellar Physiology��������������������������������������������������������������������������������������   43 Jasmine Pickford and Richard Apps Cerebellar Biochemistry/Pharmacology��������������������������������������������������������   83 Takahiro Seki Genetics of Dominant Ataxias������������������������������������������������������������������������  115 Ashraf Yahia and Giovanni Stevanin Autosomal and X-Linked Degenerative Ataxias: From Genetics to Promising Therapeutics������������������������������������������������������������������������������  141 Anya Hadji, Aurélie Louit, Vincent Roy, Mathieu Blais, François Berthod, François Gros-Louis, and Nicolas Dupré  Seeking Therapies for Spinocerebellar Ataxia: From Gene Silencing to Systems-­Based Approaches������������������������������������������������������������������������  183 Rachael Powers, Henry Paulson, and Sharan Srinivasan  Channel Genes and Ataxia ����������������������������������������������������������������������  209 Ion Mahesh Padmanaban and Christopher M. Gomez Part II Biomarkers and Tools of Trials  How to Design a Therapeutic Trial in SCAs��������������������������������������������������  265 Caterina Mariotti, Mario Fichera, and Lorenzo Nanetti  Therapy Development for Spinocerebellar Ataxia: Rating Scales and Biomarkers ������������������������������������������������������������������������������������������������������  291 Chih-Chun Lin and Sheng-Han Kuo ix

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 Clinical Rating Scales for Ataxia��������������������������������������������������������������������  317 Tanja Schmitz-Hübsch Scale for Ocular Motor Disorders in Ataxia (SODA): Procedures and Basic Understanding��������������������������������������������������������������������������������  347 Aasef G. Shaikh, Ji-Soo Kim, Caroline Froment, Yu Jin Koo, Nicolas Dupre, Marios Hadjivassiliou, Jerome Honnorat, Sudhir Kothari, Hiroshi Mitoma, Xavier Rodrigue, Jeremy Schmahmann, Bing-Wen Soong, S. H. Subramony, Michael Strupp, and Mario Manto  Cerebellar Learning in the Prism Adaptation Task��������������������������������������  363 Takeru Honda and Hidehiro Mizusawa Blood and CSF Biomarkers in Autosomal Dominant Cerebellar Ataxias��������������������������������������������������������������������������������������������������������������  379 Giulia Coarelli and Alexandra Durr Part III Autosomal Dominant Cerebellar Ataxias  Riluzole in Progressive Cerebellar Ataxias����������������������������������������������������  395 Silvia Romano, Carmela Romano, Emanuele Morena, Fernanda Troili, Agnese Suppiej, Marco Salvetti, and Giovanni Ristori  ASOs Against ATXN2 in Preclinical and Phase 1 Trials������������������������������  403 Stefan M. Pulst Antisense Oligonucleotide Therapy Against SCA3��������������������������������������  417 Hayley S. McLoughlin Spinocerebellar Ataxia Type 7: From Mechanistic Pathways to Therapeutic Opportunities ������������������������������������������������������������������������  433 Pawel M. Switonski and Albert R. La Spada  Experimental Neurotransplantation for Cerebellar Ataxias ����������������������  469 Jan Cendelin, Annalisa Buffo, Hirokazu Hirai, Lorenzo Magrassi, Mario Manto, Hiroshi Mitoma, and Rachel Sherrard Development of Mesenchymal Stem Cells Therapy for the Treatment of Polyglutamine SCA: From Bench to Bedside ������������������������������������������  499 Chih-Yuan Ho, Hsiu-Yu Lai, Ling-Mei Wang, and Bing-wen Soong Cerebello-Spinal tDCS as Rehabilitative Intervention in Neurodegenerative Ataxia��������������������������������������������������������������������������  531 Alberto Benussi, Valentina Cantoni, Alvaro Pascual-Leone, and Barbara Borroni  Cerebellar Transcranial Magnetic Stimulation in Cerebellar Ataxias ������  543 Carina França and Rubens Gisbert Cury

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Physical Therapy in Cerebellar Ataxia����������������������������������������������������������  561 Jennifer L. Keller Part IV Autosomal Recessive Cerebellar Ataxias Recent Advances on Therapeutic Approaches for Friedreich’s Ataxia: New Pharmacological Targets, Protein, and Gene Therapy����������  575 Deepika M. Chellapandi, Valentine Mosbach, Marie Paschaki, and Helene Puccio  Therapeutic Use of Interferon Gamma in Friedreich Ataxia����������������������  605 Andrea Martinuzzi, Gabriella Paparella, Marinela Vavla, Maria Grazia D’Angelo, Filippo Arrigoni, and Roberto Testi Metabolic Treatments of Cerebellar Ataxia��������������������������������������������������  629 Fanny Mochel  Clinical Trials in Fragile X-Associated Tremor/Ataxia Syndrome��������������  649 Erin E. Robertson, Joan A. O’Keefe, and Deborah A. Hall Part V Sporadic Ataxias  Therapeutic Strategies in Immune-­Mediated Cerebellar Ataxias��������������  665 Marios Hadjivassiliou, Mario Manto, and Hiroshi Mitoma  Coenzyme Q10 in Multiple System Atrophy ������������������������������������������������  679 Jun Mitsui and Shoji Tsuji  State of the Art and History of Therapeutics in Ataxias������������������������������  691 Chase Kingsbury, Shaila Ghanekar, Yangxin Huang, Yayi Zhao, Tetsuo Ashizawa, Sheng-Han Kuo, Clifton L. Gooch, and Theresa A. Zesiewicz Index������������������������������������������������������������������������������������������������������������������  723

Part I

Basic Science of Cerebellum and Ataxias

Functional Anatomy of the Cerebellum Izumi Sugihara , Yuanjun Luo, and Richard Nana Abankwah Owusu-Mensah

Abstract  This chapter revises the neuronal connections and their functional consequences in the mammalian cerebellum based on a new understating of its basic comparative and circuit-level anatomy. The transverse lobular structure and the longitudinally striped arrangement are both essential in understanding the functional organization of the cerebellum. Cerebellar neuronal circuitry has been revealed at the level of single axons. The intricate distribution pattern of zebrin-positive and zebrin-negative Purkinje cells represents the longitudinally striped organization, which is linked to the topographic axonal projection patterns of climbing fibers and Purkinje cells. On the contrary, mossy fibers show distinct axonal projection patterns more or less related to lobules. The cerebellar outputs from different parts of the cerebellar nuclei project to the cerebellar-recipient thalamic nuclei and other targets to be involved in motor and non-motor functions. Tentatively, the cerebellar cortex has some nine divisions of different functional localization related to the region-specific axonal projection patterns. Keywords  Mouse · Marmoset · Human · Zebrin · Aldolase C · Climbing fibers · Mossy fibers · Purkinje cells · Functional localization

I. Sugihara (*) Department of Systems Neurophysiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan Center for Brain Integration Research, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan e-mail: [email protected] Y. Luo · R. N. A. Owusu-Mensah Department of Systems Neurophysiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_1

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1 Introduction This chapter aims to describe the standard functional anatomy of the cerebellum in a systematic concise way and to provide enough anatomical basis to understand the rest of the book. Many new findings, including output projections based on viral vector tracings, the longitudinally striped organization of the cerebellar cortex, and functional localization based on human imaging studies, have been covered within the context of the systematic description. However, morphological development is beyond the scope of this chapter. For simplicity, we use “primate(s)” to mean “non-human primate(s)” in this chapter.

2 Macroscopic Anatomy of the Cerebellum 2.1 Outer Shape and Orientation The human cerebellum is a dumbbell-shaped structure located inferior to the occipital lobe of the cerebrum and posterior to the brain stem. The human cerebellum is connected to the upright brain stem with horizontally running cerebellar peduncles in the anterior aspect. In the rodent, the cerebellum is roughly diamond-shaped and widely exposed in the dorsocaudal part of the brain. Its ventral aspect is connected to the horizontal brain stem with vertically-running cerebellar peduncles. Thus, the spatial orientation of the cerebellum is different, approximately 90° between the human and the rodent. Consequently, the anterior, posterior, superior, and inferior directions in the human cerebellar anatomy are the equivalence of ventral, dorsal, rostral, and caudal directions in the rodent cerebellar anatomy, respectively. However, the primate’s cerebellum orientation is the intermediate between humans and rodents. The cerebellar orientation in mammals may therefore suggest an evolutional link in  locomotion posture among mammals, from quadrupedalism to bipedalism in tetrapods. In the following, the terms “anterior, posterior, superior, and inferior” are used for the human cerebellum, whereas the terms “ventral, dorsal, rostral, and caudal” are used for the animal cerebellums (mainly for the rodent cerebellum). The highly foliated cerebellar cortex unfolds to half the area of the cerebral cortex. However, it has only one-tenth the size of the cerebrum. In the posterior (dorsal, in animals) and inferior (caudal) aspects, the cerebellar surface is divided into the medial part (vermis) and the lateral parts (hemisphere, or paravermis and hemisphere) by the longitudinal dent in which the paravermal vein runs. These two divisions can be extended to the superior (rostral) aspects where the paravermal vein is absent. The whole lateral part may be designated as the hemisphere, as done usually in human cerebellum literature. Alternatively, suppose the topographic corticonuclear projection is considered. In that case, the lateral part is divided into the

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paravermal area (or pars intermedia), which projects to the interpositus nucleus, and the hemisphere, which projects to the lateral (dentate) nucleus. However, there is no visible landmark (imaginary longitudinal line) in the cerebellar surface for the boundary between the paravermal and hemispheric parts. The characteristics described above are analogous in all mammals except for the relative sizes of the vermis and the hemisphere. The remarkably large hemisphere is reflected in the dumbbell shape in the human cerebellum.

2.2 The White Matter of the Cerebellum The cerebellar white matter is the structure that connects the cerebellum to the brain stem and physically supports the entire cerebellum’s integrity. The lobular organization of the cerebellar cortex is reflected in the arborization pattern of the white matter. The major cerebellar white matter mainly runs transversely in the deepest portion of the cerebellum, mainly rostrally and partly dorsally to the cerebellar nuclei. This white matter is continuous to the middle cerebellar peduncles in the most lateral portion rostrolateral to the lateral nucleus. The middle cerebellar peduncle (or brachium pontis) is the pathway mainly for mossy fiber axons from the pontine nucleus and nucleus reticularis tegmenti pontis. Next, at the portion rostral to the lateral nucleus, this white matter is continuous to the inferior cerebellar peduncle. The inferior cerebellar peduncle (or the restiform body) is the pathway for almost all mossy fiber axons from the medulla and spinal cord and for climbing fiber axons (except for pontine-pathway spinocerebellar axons and some climbing fiber axons; see below). Ventral to this transversal bundle of the white matter, the longitudinally running bundle of the white matter starts from the hilus of the cerebellar nuclei, and its exit forms the superior cerebellar peduncle (or brachium conjunctivum). At the position where the superior cerebellar peduncle exits the cerebellum, white matter of a transverse axonal bundle covers the superior cerebellar peduncle dorsally. This transversal bundle contains a population of inferior olive (IO) axons and a pontine-­ pathway population of spinocerebellar axons, which preferentially project to the vermal area of the anterior lobe (Sugihara et al. 1999; Luo et al. 2018; Zhang et al. 2021). This axonal bundle is distinct from the superior cerebellar peduncle; it may be regarded as the rostral extension of the inferior cerebellar peduncle. There is the maintenance of somatotopic lateralization in the spinal cord, dorsal column nuclei, and the cerebellum. However, the lateralization is opposite in the brain stem nuclei. With this, input axons originating from brain stem nuclei, except for the dorsal column nuclei, often cross the midline before entering the peduncle. Likewise, output axons also generally cross the midline after exiting the superior cerebellar peduncle.

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2.3 Cerebellar Nuclei The cerebellar nuclei are relatively small gray matter embedded in the deep central area of the cerebellar white matter. In the human and primate cerebellum, four nuclei—the fastigial nucleus (medial nucleus [MN]), globose nucleus (posterior interpositus nucleus), emboliform nucleus (anterior interpositus nucleus), and dentate nucleus (lateral nucleus)—are well separated from one another by the white matter. These nuclei are partly continuous with recognizable boundaries in rodents. In humans, the dentate (lateral) nucleus is much larger than other nuclei and consists of a bag-shaped single sheet with numerous indentations. The anterior (microgyric) part of the dentate nucleus has a finer indentation than the posterior (macrogyric) part (Yamaguchi and Goto 1997).

2.4 Cerebellar Lobules in the Vermis The degree of cerebellar surface foliation is significantly different among mammals. However, the major (deep) foliation pattern of the vermis has a high similarity among all mammals in the midsagittal section (Fig. 1a). Comparative morphological studies by Larsell (1970), and Larsell and Jansen (1972), have defined ten lobules (lobules I–X, from the rostral end to the caudal end) in the vermis of various mammals and humans. Subsequent publications adopted Larsell’s definition (human: Schmahmann et al. 1999; macaque: Madigan and Carpenter 1971; Paxinos et  al. 2009; marmoset: Fujita et  al. 2010; Paxinos et  al. 2011; rat: Voogd 2004; Paxinos and Watson 2017; Swanson 1998; mouse: Marani and Voogd 1979; Paxinos and Franklin 2019; Fujita et al. 2014). Comparative identification of vermal lobules is rather straightforward based on their relative size, shape, and position across various mammals, including rodents, primates, and humans (Luo et al. 2017). They are divided into two groups, lobules I–V (anterior lobe) and lobules VI–X (posterior lobe), by the most profound fissure described as the primary fissure. Each of the two groups is then divided into individual lobules by other deep fissures, except that fissures between lobules I and II, between lobules IV and V, and between vermal lobules VI and VII are not as deep as others. Since the fissures that divide these neighboring lobules are shallow, these neighboring lobules are sometimes regarded as a combined single lobule (lobules I and II, lobules IV and V, lobules VI and VII). Indeed, “lingula cerebelli” is regarded as composed of lobules I and II, which are not distinguishable in the human cerebellum. Vermal lobules VI and VII are divided into multiple sublobules (folia) by relatively shallow fissures. The subdivisions of lobules VI and VII are complicated due to the inconspicuous fissure between  them,  and inconsistent foliation patterns in these lobules among mammals. Therefore, there may be some inconsistency in the definition and the nomenclature of lobules among different mammalian species. We have proposed that rodent (sub)lobule VIa and (sub)lobules VIb–c  +  VII are

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Fig. 1  Lobular organization of mammalian cerebellums. (a–c) Midsagittal (a), hemispheric sagittal (b), and horizontal (c) sections of the human, macaque (Macaca mulatta), marmoset (Callithrix jacchus), rat (Long-Evans), and mouse (C57BL/6N) cerebellums (from left to right). (Putatively) homologous lobules are labeled in the same color in the central vermal area. Note inconsistent names in lobules VI–VII between primates and rodents. Human and mouse section drawings in (a) were based on our samples. Abbreviations: I–X, lobules I–X; A, anterior; AA, ansiform area; C, caudal; Cr I, crus I; Cr II, crus II; D, dorsal; Fl, flocculus; HVI–HVIII, hemisphere lobules VI– VIII; I, inferior; L, lateral; M, medial; P, posterior; Par, paramedian lobule; PFl, paraflocculus; R, rostral; S, superior; Sim, simple lobule; V, ventral. (Marmoset and rat drawings in (a) were based on the data used in the figures of Fujita et al. (2010). The macaque section in (a) was redrawn from Larsell (1953). Drawings in (b) and (c) were modified from Luo et al. (2017). The lobule nomenclature is derived from Schmahmann et al. (1999) for human, Paxinos et al. (2009) for macaque, Paxinos et al. (2011) for marmoset, Paxinos and Watson (2017) for rat, and Paxinos and Franklin (2019) for mouse)

equivalent to primate (and human) lobules VI and VII in the vermis, respectively (Fujita et al. 2010; purple, red, orange, and yellow areas in Fig. 1a). For correct identification of lobules among different animal species, not only position and shape but also afferent and efferent connection patterns and longitudinally striped patterns should be considered.

2.5 Lobules in the Hemisphere As in the vermal lobules, the basic organization in the hemispheric lobules is regarded as commonly shared by all mammals (Bolk 1906; Larsell 1970). However, species-dependent differences in lobular structure are greater in the hemisphere compared to the vermis.

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The hemispheric lobules are lateral protrusions of the vermal lobules in the case of lobules I–VI, except that lobule I hardly forms a lateral protrusion and that lobule II protrudes to a limited extent. Since lobule VI (or lobule VIa in rat and mouse) simply extends laterally as compared with the caudally neighboring crus I (below), it has been designated as simple lobule (Bolk 1906). The same hemispheric lobule is also designated as “hemispheric lobule VI (HVI)” (Larsell 1970; Schmahmann et al. 1999) in primates and humans. Crus I and crus II (terms originating from Bolk 1906) are the two major lobules expanded most laterally in the central part of the cerebellar hemisphere in all mammals including humans. The paramedian lobule or lobule HVIIB is located caudal to crus II. Then, the apparently most caudal hemispheric lobule is the lateral extension of vermal lobule VIII, named “copula pyramidis” in rodents, “sublobule p of the paramedian lobule” in primates, and lobule HVIII in humans. The outer shape of these hemispheric lobules (crus I, crus II, HVIIB, HVIII) is significantly different among mammals and humans. Concerning the definition of hemispheric lobules, we have proposed that crus I in the mouse and rat is homologous with the combination of crus I and crus II in primates and humans (designated as the “ansiform area”; Luo et al. 2018). In the human cerebellum, crus I, crus II, lobule VIIB, lobule VIIIA, and lobule VIIIB have been defined in the posterior hemisphere based on thorough three-dimensional observation of lobule and fissure structures in magnetic resonance imaging (MRI) data and comparison with earlier nomenclatures (Schmahmann et al. 1999). However, the homology between human and primate cerebellar lobules in this area does not seem to be fully obvious. The ansoparamedian fissure that separates lobule HVIIB from crus II is less clear than the prepyramidal/prebiventer fissure that separates lobule HVIIB from lobule HVIIIA in the human cerebellum. Therefore, there seems to be the possibility that the combination of crus II and lobule HVIIB in the human cerebellum may be homologous to primate crus II. Axonal connection analysis by tractography from an MRI image (Steele et al. 2017) would provide some information about lobule homology in the future. The volume of the ansiform area relative to the whole cerebellum increases significantly in dexterous mammals (Luo et al. 2018; Fig. 1b,c). In the human cerebellum, the increased volume of the ansiform area makes the remarkable expansion of the cerebellar hemisphere. The paraflocculus (lobule HIX) and flocculus (lobule HX) are generally regarded as the hemispheric part of lobules IX and X, respectively. However, their cortices are joined and connected to the caudolateral edge of HVIII rather than vermal lobules IX and X in the mammalian cerebellum. In the human cerebellum, the paraflocculus occupies most of the tonsil, which is located at the medial corner between the medulla and posterior vermis. In rodents and other animals, the paraflocculus is a peculiar bulb-like protrusion in the ventrolateral part of the cerebellum (Panezai et al. 2020). This protrusion fits into the arcuate fossa surrounded by the anterior semicircular canal (Panezai et al. 2020). The flocculus is located ventromedial to the paraflocculus at the lateral or rostrolateral junction between the medulla and the cerebellum. In humans and primates, the flocculus is separately protruded from the paraflocculus, whereas they are neighboring in rodents.

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2.6 Unfolded Schemes of the Cerebellar Cortex The deeply foliated surface of the cerebellum forms a continuous single sheet of the cerebellar cortex. Consequently, the entire cerebellar cortex can be shown schematically in an unfolded scheme in the two-dimensional space. Early unfolded schemes were used to show the positional relationship of lobules and map the somatotopic representation (Bolk 1906; Larsell 1970; Snider and Eldred 1952). Later, two-­ dimensional unfolded schemes that reflected actual lobular size and continuity were created for various mammals (mouse: Fujita et al. 2014; Sarpong et al. 2018, Fig. 2c; rat: Sugihara and Shinoda 2004; Ruigrok 2003; marmoset: Fujita et  al. 2010, Fig. 2b; human: Diedrichsen and Zotow 2015, Fig. 2a). Since the original three-­ dimensional shape of cerebellar lobules is complicated, some deformation is inevitable in forming any two-dimensional scheme. Nevertheless, unfolded schemes are useful in mapping and comparing lobular dimensions. The lobular stripe patterns (Fig. 2b,c), distribution of axonal terminals, longitudinal stripe patterns, local activities recorded in functional MRI (fMRI), and schematic of functional localization can be mapped in these two-dimensional schemes, facilitating understandability.

Fig. 2  Unfolded schemes of the cerebellar cortex of human (a), marmoset (Callithrix jacchus) (b), and mouse (c). Abbreviations: I–X, lobules I–X; a–c, A, B, sublobules a–c, A, B; C, caudal; Cop, copular pyramids; Cr I, crus I; Cr II, crus II; dPFl, dorsal paraflocculus; Fl, flocculus; HI–HX, hemispheric lobules I–X; Lt, left; PFL, paraflocculus; Par, paramedian lobule; R, rostral; Rt, right; Sim, simple lobule; vPFl, ventral paraflocculus. (The human scheme is based on Diedrichsen and Zotow (2015) with permission. The marmoset and mouse schemes, in which the zebrin pattern is mapped, are reproduced from Fujita et al. (2010) and Sarpong et al. (2018))

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3 Neuronal Components and Circuitry of the Cerebellum The cerebellar cortex is composed of three layers—molecular layer, Purkinje cell (PC) layer, and granular layer, from the surface to the deep white matter. Neuronal components are uniquely positioned in these layers. The basic neuronal components and neuronal circuit organization, which have been long known in the cerebellar cortex (Ramón y Cajal 1911; Eccles et al. 1967; Fig. 3a), are uniform throughout the

Fig. 3  Neuronal composition of the cerebellar cortex. (a) Schematic drawing of neuronal components and their basic synaptic connections in the cerebellar cortex. “+” and “−” indicate excitatory and inhibitory synaptic connections. (b) Aldolase C (Aldoc, zebrin) expression in Purkinje cells. An image of Aldoc immunostaining in a coronal section of the mouse caudal cerebellum is shown. Brown, immunostaining reaction; Blue, thionine counterstaining. Abbreviations: BC, basket cell; CF, climbing fiber; CN, cerebellar nucleus; ECuN, external cuneate nucleus; IO, inferior olive; LRN, lateral reticular nucleus; MF, mossy fiber; NRTP, nucleus reticularis tegmenti pontis; PC, Purkinje cell; PN, pontine nucleus; PF, parallel fiber; SC, stellate cell

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cerebellar cortex. However, some regional variations exist in some detailed aspects. Different cell types and their regional variations have been confirmed in a transcriptomic analysis (Kozareva et al. 2021).

3.1 Purkinje Cells and Climbing Fibers Purkinje cells are the sole output component of the cerebellar cortex. Their somata are placed in a two-dimensional sheet, i.e., the Purkinje cell layer, in close proximity to each other. They have round soma, generating an axon from the basilar pole and one or two thick dendrite(s) from the apical pole. The Purkinje cell dendrite forms a fan-shaped dendritic arbor by multiple branching (Fig. 4a). By mechanisms that are not entirely clarified, the Purkinje cell dendritic arbor is usually strictly arranged in a single sheet in which the domain of each dendritic branch does not overlap with each other. The dendritic field is classified into two areas of different innervation: proximal dendrites, which indicate the thick proximal parts of the dendritic arbor, receive the climbing fiber innervation, whereas distal dendrites, which indicate the thin distal parts of the dendritic arbor, receive the parallel fiber innervation at dendritic spines. One or a few local recurrent collaterals that distribute in the Purkinje cell layer and superficial granular layer (Fig. 4a) have been observed in 92% of Purkinje cells (Sugihara et al. 2009). The target area of most Purkinje cell axons is a localized part of the ipsilateral cerebellar nucleus (CN) (Fig. 4b–d). Some Purkinje cell axons or axonal branches project to extracerebellar targets such as vestibular nuclei and the parabrachial nucleus. A Purkinje cell is innervated by one climbing fiber, which is one of the major input axons in the cerebellar cortex originating from the inferior olive (Llinás 2014). A climbing fiber reaches the soma of a Purkinje cell from the granular layer, giving rise to sparse terminals around the soma. Great branching and dense termination of a climbing fiber start at the rise of proximal dendrites (Fig. 4h, j). One climbing fiber has as many as 250 terminals (rat: Sugihara et al. 1999) and produces a large synaptic current in Purkinje cell proximal dendrites. Thus, an action potential of a climbing fiber triggers a complex spike response in the innervated Purkinje cell, in contrast to simple spikes, which are intrinsic action potentials generated at the axon initial segment of the Purkinje cell. Given the molecular expression profile and basic physiological properties, Purkinje cells are grouped into heterogeneous populations (Viet et al. 2021). The most frequently mentioned populations are zebrin (aldolase C)-positive and zebrin-­ negative populations that are distributed in alternating longitudinal stripes in the cerebellar cortex (Fig. 3b).

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Fig. 4  Afferent and efferent projections of the cerebellum. (a–c) Images of Purkinje cell axons of the rat. Dendritic arbor, soma, and axon collaterals (a), terminal arbor in the cerebellar nucleus (b), and terminals that surround a cerebellar nucleus neuron (c) are shown (Images and data are reproduced from the original data of the study published in Sugihara et al. (2009)). (d) Lateral view of two reconstructed single Purkinje cell axons that terminate in the dorsolateral hump of the anterior interpositus nucleus. (e) Image of labeled mossy fiber axons originating from the external cuneate nucleus of rat. (f) Magnified image of mossy fiber terminals of spinocerebellar axons of mouse. (g) Reconstructed single mossy fiber axon originating from the external cuneate nucleus of rat (Images and data in (f) and (g) are reproduced from the original data of the study published in Quy et al. (2011)). (h) Reconstructed single climbing fiber axon of rat. (i) Distribution of the axon shown in (h) in the whole-mount image of the rat cerebellum. (j) Image of labeled climbing fiber terminal (Images and data in (h)–(j) are reproduced from the original data of the study published in Sugihara et  al. (1999)). Abbreviations: C, caudal; CN, cerebellar nucleus; CP, caudal pole of the lateral nucleus; D, dorsal; DLH, dorsolateral hump of the anterior interpositus nucleus; ECuN, external cuneate nucleus; IO, inferior olive; Lt, left; R, rostral; Rt, right; V, ventral

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3.2 Molecular Layer Interneurons Basket cells (BCs) and stellate cells (SCs) are GABAergic neurons designated together as “molecular layer interneurons,” but generally morphologically distinguishable from each other (Sultan and Bower 1998). Somata of basket cells and stellate cells are located in the deeper one-third and outer two-thirds of the molecular layer, respectively (Brown et  al. 2019). Their dendrites receive an excitatory synaptic connection from parallel fibers. They also receive “spill-over” excitation by climbing fibers (Malhotra et al. 2021). Their axons form local arborization and innervate multiple Purkinje cells at the soma (in case of basket cells) and the dendrite (in case of stellate cells). The terminal arbor of basket cells densely surrounds single Purkinje cells at the soma and particularly at the initial axonal segment (called “pinceau”: Ramón y Cajal 1911), enabling effective inhibition of Purkinje cell activity through GABA release and electrical field interaction (ephaptic inhibition: Blot and Barbour 2014). Stellate cell activity decreases the firing regularity of Purkinje cell simple spikes, whereas basket cell activity decreases the firing frequency of Purkinje cell simple spikes (Brown et al. 2019).

3.3 Granule Cells, Parallel Fibers, and Mossy Fibers The granular layer is densely packed with granule cells. They have small soma, three to five short dendrites, and a vertical axon. Mossy fibers are the most abundant input in the cerebellar cortex, originating from diverse areas and nuclei. A mossy fiber has large ellipse-shaped terminals (long diameter, ~10 μm) with many irregular bulges and dents (“rosette” terminal) at its ends, branching points, and path (Fig. 4e, f). A rosette terminal is densely packed by dendrites of multiple nearby granule cells, to which it makes a synaptic contact, forming the structure called “glomerulus.” Axons of granule cells are non-myelinated and run straight upward to the molecular layer in which it bifurcates into a parallel fiber in a T-shape. The parallel fiber runs straight in the transverse direction in the molecular layer for a length of about 2 mm in the mouse (Huang et al. 2006), which can cross several zebrin stripes (see later section). The ascending granule cell axon and the parallel fiber make en-­ passant synaptic contacts to Purkinje cell distal dendrites and dendrites of the stellate, basket, and Golgi cells. The molecular layer is densely packed by (1) a bundle of parallel fibers, (2) plates of Purkinje cell dendritic arbors, which cross parallel fibers at a right angle (Fig. 3a), (3) stellate and basket cells and Golgi cell dendrites, and (4) processes of Bergmann glia cells. Parallel fibers do not make functional synaptic contact with all Purkinje cells they cross. Furthermore, the population of granule cells that forms effective synaptic contact with a given Purkinje cell through the parallel fiber–Purkinje cell synapse is distributed in patches in the nearby granular layer (Spaeth et al. 2022).

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3.4 Golgi Cells and Other Inhibitory Cells in the Granular Layer Golgi cells are the most abundant type of inhibitory interneurons in the granular layer, although their number is about one-thousandth of the number of granule cells (D’Angelo and Casali 2013). They are located at any depths of the granular layer but more frequently in the superficial than in the deep granular layer. They have basal and apical dendrites to receive excitatory inputs from parallel fibers and ascending granule cell axons (Cesana et al. 2013) and from mossy fiber terminals. Their inhibitory inputs are from mixed GABA-glycinergic nucleocortical projection neurons (Ankri et al. 2015) and possibly from other Golgi cells, Lugaro cells, and Purkinje cell axon collaterals. Golgi cells also receive dendrodendritic electrical synapses from thier kind nearby and project their axons to granule cell dendrites in the glomerulus. Lugaro and globular cells are GABAergic neurons located in the superficial granular layer, often immediately underneath the Purkinje cell layer, whereas candelabrum cells are GABAergic neurons located in the Purkinje cell layer (Lainé and Axelrad 1994). Lugaro cells are fusiform-shaped and smaller than Purkinje and Golgi cells in size, while globular cells are round-shaped and smaller than Lugaro cells. These three types of cells receive Purkinje cell collaterals and other inputs and project to dendrites of molecular layer interneurons. In addition, Lugaro cells receive climbing fiber axon collaterals and  project to Golgi cell  dendrites in the molecular layer. Unipolar brush cells are excitatory local neurons in the granular layer of the nodulus and flocculus. They have a single brush-shaped large and short dendrite contacting a mossy fiber and giving rise to a local mossy fiber axon.

3.5 Glial Cells in the Cerebellar Cortex The molecular layer of the cerebellar cortex contains a specific organization of glial cells. Bergmann glial cells are specialized astrocytes located mainly in the Purkinje cell layer and occasionally in the deep molecular layer near the Purkinje cell layer. They have multiple vertical processes that ascend the molecular layer straight to reach the surface of the cerebellar cortex. Abundant shorter processes are given from the cell body and the vertical process. NG2-glia cells (or oligodendrocyte precursor cells) are another population of glial cells in the molecular layer (Lin et al. 2005). Glia cell populations in the granular layer, white matter, and cerebellar nuclei are not remarkably different from those in other gray matter or white matter of the brain.

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3.6 Neurons in the Cerebellar Nuclei The cerebellar nuclei contain various types of excitatory and inhibitory neurons. They are heterogeneous regarding morphology, projection, neurotransmitter, and molecular expression (Uusisaari et al. 2007; Fujita et al. 2020). The distribution of types of neurons in the cerebellar nuclei is partly related to the compartmentalization determined by the topography of the Purkinje cell axonal projections. The major group consists of glutamatergic output neurons, classified into heterogeneous populations based on differences in size, location, molecular expression profiles, electrophysiological properties, topographic innervation, and projection patterns. These properties are correlated and define five populations in the mouse medial nucleus (Fujita et al. 2020). Inhibitory neuronal populations consist of GABAergic neurons that project to the inferior olive, glycinergic neurons that project to the brain stem (Bagnall et al. 2009), and mixed GABA-glycinergic neurons that project to the cerebellar cortex, mainly to Golgi cells (Ankri et al. 2015).

4 Afferent Axonal Projections of the Cerebellum Compared to the abundant axonal projections between areas in the cerebral cortex, the cerebellum does not have any long-distance intercortical projections of cortical neurons. Thus, all long-distance axonal projections in the cerebellum are described as the afferent (input) and the efferent (output). Their topographic patterns characterize the morphological organization of afferent and efferent projections. In other words, both the cerebellar cortex and nuclei are characterized by their compartmentalization related to the afferent and efferent axonal projection patterns. In the cerebellar cortex, compartmentalization is represented by transverse lobules and longitudinal stripes (Sugihara 2021).

4.1 Climbing Fibers Originating from the Inferior Olive The inferior olive is the multilamellar nucleus located in the caudal and medial medulla immediately dorsal to the pyramidal tract. Virtually, all neurons of the inferior olive project to the cerebellum as climbing fibers. A single inferior olive axon branches into seven climbing fibers on average in the rat and gives rise to collaterals to terminate in the cerebellar nucleus (Sugihara et al. 1999). Climbing fibers originating from a single olivary axon usually terminate in a longitudinally band-shaped area in a single lobule, multiple neighboring, or separate lobules (Sugihara et al. 2001). It is often observed that some branches project to rostral lobules and other branches project to caudal lobules from a single olivary axon (lobules I–VIa versus

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lobule VIII, or simple lobule versus crus II and paramedian lobule; Sugihara et al. 2001). When they terminate in multiple lobules, the longitudinal band-shaped termination areas in different lobules are located in a similar mediolateral position (Sugihara et al. 2001; Fig. 4h, i). Thus, the climbing fiber signals are provided directly to specific Purkinje cells located in strictly defined areas and produce significant modulatory and plastic effects in the target Purkinje cells. Therefore, the topographical projection patterns of inferior olive afferents and olivocerebellar climbing fiber axons seem to have an important role in producing functional localization of the cerebellum.

4.2 Mossy Fiber Axons Mossy fibers are the largest population of axons in the cerebellar white matter. Most mossy fiber axons originate from the so-called precerebellar nuclei in the brain stem and the spinal cord. The major precerebellar nuclei are the pontine nucleus and nucleus reticularis tegmenti pontis, both located in the pons, the lateral reticular nucleus, and the external cuneate nucleus, also located in the medulla. Almost all neurons in these nuclei project to the cerebellum as mossy fiber axons. Besides, the vestibular nuclear complex, the trigeminal nucleus, the gracile and cuneate nucleus, and medullary and pontine reticular formation contain many neurons that give rise to mossy fiber axons. In the spinal cord, the central cervical nucleus in the cervical segments, Clarke’s column nucleus in the thoracic and upper lumbar segments, border cells in the ventral horn of lumbar segments, and Stilling’s nucleus in the sacral segments are the main sources of mossy fibers (Luo et al. 2017, 2020; Zhang et al. 2021). However, many other populations of spinocerebellar neurons exist in various areas of the spinal cord gray matter, e.g., marginal Clarke’s column neurons in the thoracic segments (Luo et al. 2017). Mossy fiber axons usually innervate only the cerebellum; in other words, they do not branch before entering the cerebellum, although some lateral reticular nucleus and spinal cord axons have collaterals innervating the vestibular nucleus and other brain stem nuclei. In the cerebellum, the mossy fiber axons give rise to many collaterals that terminate in multiple lobules and multiple stripes, bilaterally or unilaterally. They possess mossy fiber termination as the main axonal arbor (precerebellar-­type mossy fiber axons; Fig. 4g). The number of mossy fiber terminals per axon generally ranges between 50 and 150 in the precerebellar-type axons in the mouse and rat (Wu et al. 1999; Quy et al. 2011; Luo et al. 2018; Biswas et al. 2019; Na et  al. 2019; Fig.  4g). The terminals originating from a single axon are often widely and sparsely spread but sometimes aggregated in a small cortical area (Luo et  al. 2018). Terminals of mossy fibers arising from the same and different origins converge on the dendrite of a granule cell (Huang et al. 2013). The distribution of the mossy fiber terminals is well topographically associated with the transverse lobular longitudinal stripe organization of the cerebellar cortex. The topography of the mossy fiber projection pattern is quite specific to distinct mossy

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fiber projections from different origins. Some of the mossy fiber axons project to the cerebellar nucleus with a topographic relationship, whereas some others do not project to the cerebellar nucleus. There are distinct types of axons that possess a small number of collaterals that enter the cerebellar cortex and terminate as mossy fibers (Luo et al. 2018). Mossy fiber branches of vestibular primary afferent axons (Ando et al. 2020) and mossy fiber branches of the output axons of the cerebellar nucleus neurons belong to this type (non-precerebellar-type mossy fiber axons).

4.3 Distribution Pattern of Major Mossy Fiber Axons in the Cerebellar Cortex Mossy fiber axons arising from various origins, which convey specific information, have different projection patterns (Fig. 5a). These projections are supposed to significantly contribute to the somatotopy (Fig. 5b) and functional localization of the cerebellar cortex (Sect. 7). Seminal tracing studies with Nauta’s method performed mainly in cat in the 1960s and 1970s by Grant (1962), Brodal and Hoivik (1964), and others have clarified the pathways and terminal distribution patterns of mossy fibers originating from major sources in the brain stem and the spinal cord (cf. Brodal 1981; Schmahmann et al. 2019). Recently, analyses of single axon morphology have revealed branching patterns and collateral projections in mossy fiber axons originating from several sources (Wu et al. 1999; Quy et al. 2011; Luo et al. 2018; Biswas et al. 2019; Na et al. 2019; Luo et al. 2020; Ando et al. 2020; Zhang et al. 2021). Mossy fiber projections from the spinal cord, dorsal column nuclei, and lateral reticular nucleus share relatively similar lobular projection patterns (Wu et al. 1999; Quy et al. 2011; Luo et al. 2018, 2020; Zhang et al. 2021), although they have different stripe preference (Gravel and Hawkes 1990). They project to vermal and paravermal areas of lobules I–V, rostral part of lobule VIa, and lobules VIII–IX, contributing to the somatotopy of limbs, trunk, and neck in these lobules. Mossy fibers from the trigeminal nucleus mainly project to the simple lobule, crus I, crus II, paramedian lobule, and vermal lobule IX (Welker 1987; Van Ham and Yeo 1992), contributing to the somatotopy of the face in these lobules. Mossy fibers from the medial vestibular nucleus project preferentially to lobules IXc–X and flocculus, moderately to lobule I, and sparsely to all vermal lobules (Ando et al. 2020). Mossy fibers from other parts of the vestibular nuclear complex, including nucleus X, also project to the cerebellum, lobules I–V in particular (Matsushita and Okado 1981). Mossy fibers from the primary vestibular sensory afferent project to lobules I, IXc, and X (Brodal 1972). The pontine nucleus sends its axons most broadly in the cerebellar cortex among precerebellar nuclei, covering all cerebellar lobules but lobule X (Brodal 1981; Biswas et  al. 2019). The pontocerebellar projection shows lobule-related topographic organization (Brodal 1979; Brodal and Bjaalie 1992). The topography of

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Fig. 5  Topographic distribution of mossy and climbing fibers in the cerebellar cortex. (a) Schematic distribution patterns of mossy fibers originating from the major origins—based on data published in rat and mouse single axon studies (Wu et al. 1999; Quy et al. 2011; Luo et al. 2017, 2020; Biswas et al. 2019; Na et al. 2019; Ando et al. 2020; Zhang et al. 2021) and tracing studies in cat and macaque in the 1960s and 1970s by Grant (1962), Brodal and Hoivik (1964), and others (cf. Brodal 1981; Schmahmann et al. 2019). (b) Schematic mapping of somatotopy in the cerebellar cortex (Cited from Khoa et al. (2021)). (c) Schematic distribution patterns of climbing fibers originating from subareas of the inferior olive—based on data published in rat and mouse studies (Sugihara and Shinoda 2004; Sugihara and Quy 2007). The right side shows the zebrin stripe pattern (Sarpong et al. 2018) for comparison. (d) Mapping of the climbing fiber distribution pattern drawn on the whole-mount preparation of the Aldoc-Venus mouse cerebellum (Modified from Luo and Sugihara (in press)). (e) Subareas of the inferior olive color-coded to show the topographic projection pattern of climbing fibers between (c) and (e), and (d) and (e); mapped on the FoxP2 immunostaining image of the mouse inferior olive (Modified from Luo and Sugihara (in press)). Abbreviations: 1+, 1−, and so on, compartments 1+, 1−, and so on; I–X, lobules I–X; a–d, sublobules or subareas a–d; A, B, C1, C2, C3, CX, D0, D1, D2, X, X–CX, names of cerebellar modules; beta, beta subnucleus of the inferior olive; C, caudal; cMAO, caudal part of the medial accessory olive; Cop, copular pyramids; Cr I, crus I; Cr II, crus II; dDAO, dorsal fold of the dorsal accessory olive; DC, dorsal cup; DM, dorsomedial subnucleus of the inferior olive; Fl, flocculus; LRN, lateral reticular nucleus; Lt, left; NRTP, nucleus reticularis tegmenti pontis; Par, paramedian lobules; PFl, paraflocculus; PN, pontine nucleus; PO, principal olive; R, rostral; Rt, right; Sim, simple lobule; vDAO, ventral fold of the dorsal accessory olive; VLO, ventrolateral outgrowth; vMAO, ventral part of the medial accessory olive

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the pontocerebellar projection is simply summarized into three groups in the mouse (Biswas et al. 2019), although further analysis may distinguish more groups. Pontine nucleus axons originating from the rostral, medial, and lateral parts, which receive projections from association cortices of frontal, parietal, and temporal lobes (Schmahmann and Pandya 1997), terminate mainly in the paraflocculus, crus I (equivalent to crus I and crus II in primates: Luo et al. 2017), and lobules VIb–c and VII. Those originating from the central part of the pontine nucleus, which receives projections from the somatosensory and motor cortices of the face and forelimb somatotopy (Leergaard et al. 2000), terminate mainly in the simplex lobule, crus II, and paramedian lobule. Those originating from the caudal part of the pontine nucleus, which receives projections from the somatosensory and motor cortices of trunk and hindlimb somatotopy (Leergaard et al. 2000), terminate mainly in lobules II–VIa, VIII, and copula pyramidis. A single axon often innervates the above combination of lobules simultaneously by the interlobular axonal branching, indicating a functional link between these lobules.

4.4 Other Afferent Projections to the Cerebellar Cortex Besides climbing and mossy fibers, the cerebellar cortex receives noradrenergic and serotoninergic projections from the brain stem. Noradrenergic axons originate from the locus coeruleus (Carlson et al. 2021), whereas serotoninergic axons originate from diverse brain stem regions and nuclei (Kerr and Bishop 1991). These axons are densely distributed in the cerebellar nuclei and all layers of the cerebellar cortex. In the molecular layer, serotoninergic fibers tend to run in the transversal direction, whereas noradrenergic fibers run in the longitudinal direction (Longley et al. 2021). These axons have “beaded” abundant varicosities along their path to release noradrenaline and serotonin. All neurons have receptors to these monoamines and produce responses when tested. At the behavior level, these neurotransmitters significantly modulate plasticity and learning in motor and cognitive behaviors. The innervation morphology of these axons also shows significant plastic changes (Nedelescu et al. 2017). Some part of the cerebellum that receives orexigenic projection from the lateral hypothalamus has been reported. Some mossy fibers originating from the vestibular nucleus contain acetylcholine (Barmack et al. 1992).

4.5 Afferents of the Cerebellar Nuclei Purkinje cell axons project to a small area of the cerebellar nuclei to form the main synaptic inputs for both excitatory and inhibitory output neurons in the cerebellar nuclei (Fig. 4c), although they may project to a broader area in the medial nucleus and the vestibular nuclei (Sugihara et al. 2009).

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Almost all climbing fiber axons have collaterals terminating in the cerebellar nucleus (Fig. 4h). The termination area of collaterals of a climbing fiber axon is also localized (Sugihara and Shinoda 2007). Furthermore, this area topographically matches the termination area of axons of the Purkinje cells located in the area (or module) innervated by that climbing fiber axon (Sugihara et al. 2009). Therefore, projections of Purkinje cell axons and climbing fiber axon collaterals contribute to determining the compartmentalization of the cerebellar nuclei. Mossy fiber axons do not necessarily have collaterals terminating in the cerebellar nucleus. Axons originating from the lateral reticular nucleus, nucleus reticularis tegmenti pontis, Clarke’s nucleus, marginal Clarke’s nucleus, and the ventral horn of the spinal cord often have collaterals terminating in the cerebellar nuclei, whereas axons originating from the external cuneate nucleus (Fig. 4g), pontine nucleus, and Stilling’s nucleus rarely or infrequently have collaterals in the cerebellar nucleus (Gerrits and Voogd 1987; Wu et al. 1999; Quy et al. 2011; Luo et al. 2017, 2020; Biswas et al. 2019; Na et al. 2019; Zhang et al. 2021). A mossy fiber axon sometimes has multiple collaterals in different cerebellar nuclei or bilateral nuclei. Thus, the contribution of mossy fiber collaterals to the compartmentalization of the cerebellar nuclei seems various. Serotoninergic and noradrenergic fibers densely innervate the cerebellar nuclei. Other axons have dense termination in the cerebellar nuclei but no termination (Luo and Sugihara 2014) or a trace of mossy fiber termination (Zhang et al. 2021) in the cerebellar cortex. Although these types of axons can effectively modify the output of the cerebellar nuclei neurons, they have not been much clarified.

5 Compartments or Modules of the Cerebellum 5.1 Cerebellar Modules Determined by Projections of Climbing Fibers and Purkinje Cell Axons The topographic patterns of the projections of climbing fibers and Purkinje cell axons are tightly correlated (Ruigrok et  al. 2015). Concerning this, a group of Purkinje cells located in similar mediolateral positions in different lobules convergently project to a common area within the cerebellar nucleus (Sugihara et al. 2009). Collaterals of olivary axons that project to this group of Purkinje cells then project to the same area in the cerebellar nucleus (Sugihara and Shinoda 2007). Furthermore, GABAergic neurons in this area of the cerebellar nucleus project back to the localized area in the inferior olive where those olivary axons originate (Ruigrok  and Voogd 1990). As a whole, olivary axons and Purkinje cell projections are organized into longitudinal bands in the cerebellar cortex, which is the anatomical entity of the olivocorticonuclear “microcomplexes” (Ito 2012). Note that there are exceptions to the above basic organization scheme. For example, some Purkinje cell axons project to structures outside the cerebellar nucleus, such as the medial parabrachial nucleus

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(Hashimoto et  al.  2018). Some olivary axons branch transversely to project to Purkinje cells located in mediolaterally separate stripes (Fujita and Sugihara 2013). The longitudinal bands described above are classified into several discreet groups (or “modules” as designated later: Ruigrok et al. 2015) that have a clear topographical relationship between subareas of the inferior olive and cerebellar nuclei (Voogd and Bigaré 1980; Buisseret-Delmas and Angaut 1993). At the broadest level, five gross modules (A, B, C1/C3, C2, and D modules) have been defined in the vermis, lateral vermis, medial and lateral paravermis, intermediate paravermis, and hemisphere, respectively, each with a topographically connected subarea in the IO and CN plus the lateral vestibular nucleus (Ruigrok et al. 2015). Finer classification of the modules has been identified (A, AX, X, X–CX, B, A2, C1/C3, CX, C2, D0, D1, and D2 modules: Buisseret-Delmas and Angaut 1993). The spatial distribution pattern of these modules in the cerebellar cortex matches the zebrin-striped pattern (see the next section) in the cerebellar cortex as reported in the rat and mouse (Voogd et  al. 2003; Sugihara and Shinoda 2004; Sugihara and Quy 2007; Fig.  5c–e). In other words, each zebrin stripe has distinct topographical olivocerebellar and corticonuclear projection patterns (Voogd et  al. 2003; Sugihara and Shinoda 2004; Sugihara et al. 2009; Fujita et al. 2010). A similar organization has been reported in the marmoset (Fujita et al. 2010). Inputs to subareas of the inferior olive are directly related to the functional significance of the cerebellar modules. They are described in Sect. 7 to some extent. Besides the longitudinally striped organization described above, the lobular organization has also been observed in the olivocerebellar projection. In the vermis, generally distinct groups of climbing fiber axons that branch into (1) lobules I–V (or VIa) and VIII, (2) lobule IX, and (3) lobules (VIa), VIb–c, and VII have been observed (rat: Sugihara et al. 2001; Sugihara and Shinoda 2004; mouse: Sugihara and Quy 2007). In the hemisphere, a generally distinct group of climbing fiber axons project to (1) crus I and paraflocculus, and (2) simple lobule, crus II, and paramedian lobule. However, the lobular organization of the olivocerebellar and corticonuclear projections has not been fully clarified yet.

5.2 Longitudinal Stripes of Molecular Expression in the Cerebellar Cortex Beyond the three classical longitudinal subdivisions (vermis, paravermis, and hemisphere), finer longitudinal subdivisions of the cerebellar cortex have been revealed. Purkinje cells are composed of heterogeneous populations of different molecular expression profiles. Aldolase C or zebrin II is the gold standard of such molecules in rat and mouse (Brochu et al. 1990; Voogd and Glickstein 1998; Sugihara et al. 2004; Fujita et  al. 2014; Fig.  3b). The striped distribution patterns of aldolase C (zebrin II)-positive and aldolase C-negative Purkinje cells (zebrin pattern) have been clarified in the entire cerebellar cortex in the rat and mouse (Hawkes and

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Leclerc 1987) as mapped in the unfolded scheme of the cerebellar cortex (rat: Sugihara and Shinoda 2004; Ruigrok et al. 2015; Sugihara and Quy 2007; mouse: Fujita et al. 2014; Sarpong et al. 2018; Fig. 2c). The striped distribution pattern is well conserved among individuals (Hawkes and Leclerc 1987; Fujita et al. 2014). Whereas the patterns in the rat and mouse have high similarity (Sugihara and Quy 2007; Fujita et al. 2014), the zebrin pattern in the marmoset, the sole primate species in which the zebrin pattern was clarified in the entire cerebellar cortex, shows basic characteristics shared with that of the rat and mouse as well as some unique characteristics (Fujita et al. 2010; Fig. 2b). Furthermore, all examined mammals seem to have more or less similar zebrin patterns at least in the anterior lobules (lobules I–V), where the three clear narrow positive stripes facilitate recognition (Sillitoe et al. 2005). The zebrin pattern is highly linked with the topographic connections of Purkinje cell axons and climbing fiber axons (see the preceding section). The zebrin pattern is uniquely correlated with the lobular organization; the zebrin-striped patterns in different lobules are quite distinct from one another (Fujita et al. 2014). Simply, the cerebellar cortex is roughly divided into four areas (“zones” of Ozol et al. 1999) with distinct patterns of zebrin stripes in the rat and mouse. In lobules I–V and the rostral part of lobule VIa (anterior zone of Ozol et al. 1999), zebrin-negative stripes are much wider than zebrin-positive stripes. In vermal lobule VI (posterior part) and lobule VII, zebrin-positive stripes are much wider and occupy most areas (central zone). In lobule VIII, the anterior part of IX, crus II, paramedian lobule, and copula pyramidis (posterior zone), both zebrin-positive and zebrin-negative stripes occupy substantial areas. In the posterior part of lobule IX, X, paraflocculus, and flocculus, most areas are occupied by zebrin-positive Purkinje cells (nodular zone). However, if details of the zebrin pattern are compared in the entire cerebellar cortex, the area-dependent change in the pattern is more complicated than the above classification into four types, in the hemisphere in particular. One of the remarkable features of the striped pattern in the hemisphere is that all stripes shift laterally, and positive stripes are wider and merge in crus I in the rat and mouse (Sugihara and Shinoda 2004; Sugihara and Quy 2007) and in crus I and crus II, which are equivalent to rodent crus I (Luo et al. 2017), in marmoset. Several other molecules are also expressed heterogeneously in Purkinje cell populations similar to aldolase C (zebrin). A group of molecules (e.g., EAAT4, PLCb3) shows nearly the same distribution as aldolase C. Another group of molecules such as PLCb4 shows the pattern precisely complementary to aldolase C (Sarna et al. 2006). Expression patterns of other molecules are different from the zebrin pattern to various degrees. For example, HSP25 is expressed in multiple longitudinal stripes in lobule VII, IX, paraflocculus, and flocculus in the mouse (Fujita et  al. 2014). Tyrosine hydroxylase is expressed mainly in parts of zebrin-positive stripes in lobules VII–X and copula pyramidis, paraflocculus, and flocculus (Locke et al. 2020). Pcdh10 is expressed mainly in parts of aldolase C (zebrin)-positive stripes (Sarpong et al. 2018). Heterogeneous expression of molecules is presumably involved in (1) clustering of a population of neurons and formation of topographic afferent and efferent axonal projections in the embryonic period in the case of cell adhesion molecules such as EphA4 and Pcdh10 (Fujita et al. 2012; Vibulyaseck et al. 2017;

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Tran-Anh et al. 2020), and (2) forming different synaptic responses, excitability and plasticity in the case of synaptic and signaling molecules such as EAAT4 and PLCb4 (Nguyen-Minh et al. 2019; Viet et al. 2021).

5.3 Compartmentalization of the Cerebellar Nuclei The topographic projection patterns of Purkinje cell axons and collaterals of olivocerebellar climbing fiber axons define the functional compartmentalization of the cerebellar nuclei (preceding section). Thus, the cerebellar nuclei have subdivisions that approximately correspond to the longitudinally striped organization of the cerebellar cortex (Sugihara and Shinoda 2004, 2007). However, the positional correspondence between the nuclear subdivisions and cortical stripes is not simple. In the medial nucleus (MN), Purkinje cell axons from zebrin-positive stripes terminate in the ventral (in the most medial part of the MN) or caudoventral (in the rest of the MN) part of the medial nucleus. In contrast, Purkinje cell axons from zebrin-­ negative stripes terminate in the dorsal or rostrodorsal parts. Consequently, the medial nucleus has caudoventral zebrin-positive and rostrodorsal zebrin-negative subdivisions because no other neurons other than Purkinje cell axons express zebrin in the cerebellar nuclei. The boundary between the zebrin-­ positive and zebrin-negative areas is not as clear as in the cerebellar cortex since the projection of Purkinje cell axons shows some spread beyond the boundary more or less. In the interpositus nucleus, zebrin-negative (and faintly-positive) Purkinje cell axons project to the rostrodorsal part, i.e., the anterior interpositus nucleus, and to the medial part of the posterior interpositus nucleus. In contrast, zebrin-positive Purkinje cells project to the ventral and lateral parts of the posterior interpositus nucleus. Thus, the striped arrangement of zebrin-positive and zebrin-negative stripes in the cerebellar cortex is transformed into the rostrodorsal versus caudoventral segregation of zebrin-positive and zebrin-negative areas in the medial and interpositus cerebellar nuclei (Sugihara and Shinoda 2007). Furthermore, another type of subdivision in the cerebellar nuclei seems to be connected to different lobules. For example, in the lateral part of the posterior interpositus nucleus, Purkinje cells in crus II and simple lobules project dorsally while those in crus I and paraflocculus project ventrally (Luo et  al. 2017; preliminary results). A similar tendency is present in the lateral nucleus (preliminary results of Owusu-Mensah et al.).

6 Output Projections of the Cerebellum Most of the output projection of the cerebellum originates from cerebellar nucleus neurons. In addition, some Purkinje cell axons project to the targets outside the cerebellum. Although the number of output axons of the cerebellum is much smaller

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than its afferent axons, the output axons are essential in conveying the integrated output signal of the cerebellum to other parts of the brain. A single output axon of the cerebellar nuclei generally has multiple targets by putative branching to be involved in diverse functions (Fujita et al. 2020). Transsynaptic labeling has revealed that the cerebellar output targets from any single lobule are remarkably diverse, covering multiple cerebral cortical areas and other forebrain areas (Pisano et  al. 2021). Nonetheless, output projections of the cerebellar nuclei can be classified, based on the difference in the position, molecular expression profile, the projection pattern, and the main function involved as revealed in the medial nucleus (Fujita et al. 2020).

6.1 Somatomotor System A classical view of the cerebellar somatomotor output system is that the medial nucleus mainly projects to the reticular formation and vestibular nucleus. These structures then give rise to reticulospinal and vestibulospinal descending projections, which compose the ventromedial subcortical descending motor system of the spinal cord (Lemon et al. 2012; Kuypers et al. 1962) to control the musculature of the neck, trunk, and proximal limb (extensors and flexors) for locomotion, anti-­ gravity movements, and posture. On the other hand, interpositus and lateral nuclei mainly project to the motor cortex through the ventrolateral thalamic nucleus and to the red nucleus. These structures then give rise to corticospinal and rubrospinal descending projections, which compose the cortical and lateral subcortical descending motor systems of the spinal cord (Lemon et  al. 2012; Kuypers et  al. 1962), respectively. These systems mainly control fine movements of extremities such as reach and grasp (Lemon et al. 2012). In studies in rodents, the rostral part of the medial nucleus, which receives Purkinje cell projection from zebrin-negative stripes in the vermal lobules I–VIa and VIII, is the main source for the ventromedial motor system (Fujita et al. 2020). Additionally, Purkinje cells in the lateral vermis (zebrin stripes 2-) in lobules II–VIa project to the lateral vestibular nucleus (Deiters nucleus) (Sugihara et  al. 2009), which is the origin of the lateral vestibulospinal projection. This projection also belongs to the ventromedial motor system (Kuypers et al. 1962) and is involved in controlling the flexor and extensor muscles of the limb. The anterior interpositus nucleus, which receives Purkinje cell innervation from most of the zebrin-negative stripes in the paravermal and hemispheric areas, projects mainly to the red nucleus and ventrolateral thalamic nucleus (Teune et  al. 2000). Thus, the output system of the anterior interpositus nucleus is involved in the cortical/rubral motor system. A well-known example is the control of the eyeblink conditioning performed in the zebrin-negative areas of the lateral paravermis and hemisphere in the junction between lobules V and VI, the lateral part of the anterior interpositus nucleus, and the red nucleus (Mauk et al. 2014). However, it would be worth mentioning that the anterior interpositus nucleus neurons have diverse

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targets, besides the red nucleus and ventrolateral thalamic nucleus, such as other thalamic areas, zona incerta, several midbrain, pontine, and medullary areas (Teune et al. 2000), and the spinal cord down to the lumbar segments (Sathyamurthy et al. 2020). Neurons in the dorsal part of the lateral nucleus also project to the ventrolateral thalamic nucleus (Teune et al. 2000). After being relayed by the ventrolateral thalamic nucleus, the cerebellar output pathway reaches the motor and premotor cortices, where it meets the output of the basal ganglia relayed in the ventral lateral region of the thalamus adjacent to the cerebellar-recipient ventrolateral thalamic nucleus. Some outputs from the caudoventral part of the medial nucleus and the lateral nucleus are relayed by the ventromedial and ventral anterior thalamic nucleus and project to the anterolateral motor cortex in rodents (Fujita et al. 2020; Gao et al. 2018; Chabrol et al. 2019), which is equivalent to the premotor area in primates. This pathway is involved in the preparatory activity of movements. Some outputs of the caudodorsal part of the medial nucleus project to the intralaminar thalamic nuclei, such as the mediodorsal, parafascicular, and centrolateral nuclei (Fujita et al. 2020). These thalamic nuclei project to the striatum (Chen et al. 2014) and subthalamic nucleus (mainly through the parafascicular nucleus; Watson et al. 2021; Pisano et al. 2021). This is the pathway that the cerebellum can interact with the function of the basal ganglia such as selection, initiation, and learning of somatomotor and other behaviors, and generation of involuntary movements. The parafascicular nucleus also projects to the hippocampus and the amygdala (Kang et al. 2021).

6.2 Oculomotor System A significant portion of the cerebellar output is involved in the control of the oculomotor system. Primarily, the final output of the oculomotor system is mediated mainly by six extraocular muscles innervated by three oculomotor nuclei in the brain stem. However, eye movements are controlled in multiple distinct ways, including vestibuloocular reflex (VOR), optokinetic reflex (OKR), vergence, saccade, smooth pursuit, and fixation. Different areas in the cerebellum are involved in the control of these different types of eye movements. The flocculus is involved in OKR and VOR, whereas the nodulus is involved in VOR. Purkinje cells in the flocculus and nodulus project not to the cerebellar nuclei but directly to the vestibular nuclei. The ventral part of paraflocculus is involved in the smooth pursuit in primates (Shidara and Kawano 1993), presumably relayed by the ventral part of the lateral nucleus. The caudal part of vermal lobules VI and VII (“oculomotor vermis”) is involved in vergence, saccade, and smooth pursuit, relayed by the caudal part of the medial nucleus (Fujita et al. 2020). Besides the caudal part of the medial nucleus, the ventrolateral part of the posterior interpositus nucleus is also the source of the projection to the superior colliculus (Kawamura et al. 1982), possibly involved in saccades and orienting movements. This area of the nucleus is mainly innervated by the lateral part of lobule IX (stripe 4+; Sugihara et al. 2009).

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6.3 Various Non-Motor Output Pathways The output connection of the cerebellar medial nucleus to non-motor midbrain and pontine nuclei (ventral tegmental area, interpeduncular area, periaqueductal gray, and locus coeruleus) was first reported in a cat lesioning study (Snider and Maiti 1976). Subsequent studies have demonstrated other non-motor output projections, as discussed in the following paragraphs. The ventral part of the lateral nucleus projects to the mediodorsal thalamus, which then projects to the prefrontal cortex (Middleton and Strick 2001). The prefrontal cortex is involved in various cognitive functions, emotional control, and motivation. The ventral part of the lateral nucleus is mainly innervated by the hemispheric lobules crus I and II in primates. The most ventral part of the medial nucleus innervated by vermal zebrin-positive stripes 1+ and 2+//3+ projects to the parabrachial nucleus, Kölliker-Fuse nucleus, and inferior and medial vestibular nuclei (Fujita et al. 2020). Some Purkinje cells in the lateral vermis in lobules VIII and IX directly project to the parabrachial nucleus (Hashimoto et al. 2018). These output projections may be involved in the autonomic and visceral functions. Some neurons in the medial, interpositus, and lateral nuclei project to the hypothalamus (Wen et al. 2004). The hypothalamus in return projects to the cerebellum through the pontine nucleus. This projection is involved in feeding, stress, and immune responses. Other targets of the cerebellar output include the following. Dopaminergic and GABAergic neurons in the ventral tegmental area (Snider and Maiti 1976; Carta et  al. 2019; Baek et  al. 2022), which project to the medial prefrontal cortex and other areas (Kang et al. 2021), receive projections mainly from the lateral nucleus and additionally from the posterior interpositus and medial nuclei. The periaqueductal gray (Frontera et  al. 2020; Vaaga et  al. 2020) receives projections mainly from the lateral and medial nuclei. Serotoninergic neurons in the dorsal raphe nucleus receive projections from the lateral nucleus (Pollak Dorocic et al. 2014). Noradrenergic and GABAergic neurons in the locus coeruleus are innervated by the cerebellar nuclei and some vermal Purkinje cells (Schwarz et al. 2015). Some cerebellar nucleus neurons also project to the zona incerta, substantia nigra, laterodorsal tegmental nucleus, pedunculopontine tegmental nucleus, nucleus incertus, and supramammillary region (Teune et  al. 2000; Fujita et  al. 2020; Kebschull et  al. 2020; Kang et al. 2021).

6.4 Inhibitory Output Projection from the Cerebellar Nuclei Small GABAergic neurons present in all deep cerebellar nuclei send their axons to the contralateral inferior olive through the superior cerebellar peduncle. The topographic relationship of this projection matches the topography of the olivonuclear

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and olivocorticonuclear projections (Ruigrok and Voogd 1990). This inhibitory projection terminates mainly in the dendritic region, which forms dendrodendritic gap junctions in inferior olive neurons, affecting electrical coupling between neurons. The medial nucleus contains many glycinergic neurons that project to the vestibular and reticular formation in the ipsilateral brain stem (Bagnall et  al. 2009), presumably constituting a part of the ventromedial somatomotor system (see Sect. 6.1).

7 Functional Localization in the Cerebellum Animal recording, lesioning, and genetically manipulating studies, and human imaging studies have revealed functional localization (Brodal 1981; Schmahmann et al. 2019). Topography of afferent and efferent axonal projection patterns underlies the cerebellar functional localization. The functional localization is usually described based on clear landmarks of the cerebellum, i.e., lobules and the distinction between the vermis and the hemisphere. The longitudinally striped patterns such as zebrin stripes and A–D modules (Fig. 5c) must also be considered. Indeed, climbing fiber activity in different stripes occurs in a different context in crus II of behaving mice (Tsutsumi et al. 2019). However, zebrin stripes or A–D modules have not been identified in most functional studies, or in any primate or human studies. Therefore, in this section, the lobular organization, which is mainly linked with mossy fiber projection patterns (Fig. 5a), is focused primarily. Striped or modular subdivisions (Fig.  5c) are mentioned secondly within each lobular division. It is also to be noted that multiple areas located in different functional localization domains can simultaneously control different aspects of a single motor behavior as in the case of involvement of vermis-medial nucleus in eyeblink conditioning (Wang et al. 2020).

7.1 Functional Localization of Vermal Areas 7.1.1 Lobules I–VIa and VIII Vermal lobules I–VIa and VIII (Fig. 6a, brown) are involved in the control of locomotion and posture (Jahn et al. 2008; Coffman et al. 2011; Ozden et al. 2012; Luo et al. 2020). These lobules receive information mainly related to limb, trunk, and neck deep and cutaneous sensations through mossy fiber projections from the spinal cord (Luo et al. 2017, 2020; Zhang et al. 2021), dorsal column nuclei (Quy et al. 2011), and lateral reticular nucleus. These lobules also receive mossy fiber projections from the caudal part of the pontine nucleus that mediates signals mainly from hindlimb somatotopic areas of the somatosensorimotor cortices (Biswas et al. 2019;

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Fig. 6  Topographic projection patterns of Purkinje cell axons linked with the functional localization of the cerebellar cortex. (a–d) The whole cerebellar cortex was divided into eight areas of major functional localization. The topographic projection patterns from these areas to subareas of the cerebellar nuclei are summarized (black and brown circumscribed areas and arrows). In each panel, the zebrin pattern (shaded) and divisions of topographic olivocerebellar climbing fiber projections and corticonuclear Purkinje cell projections of the mouse cerebellar cortex are shown on the left side. On the right side, rostral (top) and caudal (bottom) levels of the mouse cerebellar nuclei are drawn. Target areas of the projection are mapped in the same color as the origin in the cortex. The proposed function for each division is indicated by the arrow. The scheme is based on Sugihara and Shinoda (2004, 2007), Sugihara et  al. (2009), Sarpong et  al. (2018), Fujita et  al. (2020), and preliminary data by Owusu Mensah, Luo Yuanjun, and Izumi Sugihara. Abbreviations: I–X, lobules I–X; a–c, sublobules or subareas a–c; AIN, anterior interposed nucleus; cLN, caudal part of the lateral nucleus; cMN, caudal part of the medial nucleus; Cop, copular pyramids; Cr I, crus I; Cr II, crus II; D, dorsal; DLP, dorsolateral protuberance of the medial nucleus; d-Y, dorsal Y nucleus; Fl, flocculus; L, lateral; LN, lateral nucleus; LVN, lateral vestibular nucleus; M, medial; MN, medial nucleus; MVN, medial vestibular nucleus; Par, paramedian lobule; PFl, paraflocculus; Sim, simple lobule; PIN, posterior interposed nucleus; SVN, superior vestibular nucleus; V, ventral; vLN, vPIN, ventral part of the LN, PIN

Coffman et al. 2011). There are some lobule-dependent and stripe-dependent differences in mossy fiber projections in these lobules. Zebrin-negative stripes are much wider than zebrin-positive stripes in these areas. Climbing fibers in these lobules mainly arise from the central and lateral parts of the caudal part of the medial accessory olive (cMAO-b and cMAO-a). The output of these areas is relayed by the

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rostral and ventral parts of the medial nucleus and projects to the brain stem reticular formation and other areas (group F1R -“posturomotor”- and F3 -“positional-­ autonomic”- of Fujita et al. 2020). The most lateral part (zebrin stripe 2-//4-) of these vermal areas has a special output connection. Purkinje cells in this part project directly to the lateral vestibular nucleus (Deiters’ nucleus), where large neurons give rise to the lateral vestibulospinal tract to control ipsilateral anti-gravity muscles. 7.1.2 Lobules VIb–c and VII Vermal lobules VIb–c and VII (Fig. 6b, brown) are involved in the non-motor function and oculomotor control (Suzuki et al. 2012; Watson et al. 2014; Catz and Thier 2007). Human imaging studies show cognitive function in vermal lobules VI and VII (Guell et al. 2018). These areas receive mossy fiber projections mainly from the rostral, medial, and lateral parts of the pontine nucleus, which relay the signals from association cortices, including the medial prefrontal cortex (Biswas et  al. 2019). Zebrin-positive stripes occupy most of these areas. Climbing fibers in these areas mainly arise from the medial parts of the caudal part of the medial accessory olive (cMAO-c). Many targets in the pons, midbrain, and thalamus receive relay information from these areas through the caudodorsal parts of the medial nucleus (group F2-“orienting” of Fujita et al. 2020). 7.1.3 Lobule IXa–b Lobule IXa–b (Fig. 6c, brown) is involved in functions related to the orientation and sensory processing of the face and head (Waespe et al. 1985; Welker 1987; Sugihara 2005). These areas receive mossy fiber projections mainly from the external cuneate nucleus (Quy et al. 2011) and trigeminal nucleus (Welker 1987) and additionally from the pontine nucleus and spinal cord (Luo et  al. 2017; Zhang et  al. 2021). Zebrin-positive stripes predominate these areas. Climbing fibers in these areas mainly arise from the subnucleus beta (to medial parts of lobule IXa–b), and the caudal part of the ventral lamella of the principal olive (Sugihara and Shinoda 2004). The output of the medial parts of this lobule is relayed by the caudoventral parts of the medial nucleus and projects to many targets in the medulla, pons, midbrain, and thalamus (group F4-“vigilance” and F3 -“positional-autonomic”- of Fujita et al. 2020). The output of the lateral parts of this lobule innervates the lateral ventral part of the posterior interpositus nucleus (Sugihara et al. 2009), which then projects to the superior colliculus (Kawamura et al. 1982) and other unidentified targets.

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7.1.4 Lobules IXc and X Lobules IXc (ventral uvula) and X (nodulus) (Fig. 6b, black) are involved in the control of adaptation of vestibuloocular reflex and vestibular reflexes of head and body orientation and in motion sickness (Barmack et al. 1992; Liu and Angelaki 2009; Cohen et al. 2019). These areas receive mossy fiber projections mainly from the vestibular nucleus (Ando et al. 2020) and project output directly to the vestibular nuclei. Zebrin-positive stripes occupy almost all of these areas. Climbing fibers in these areas mainly arise from the dorsal cap and ventrolateral outgrowth (Sugihara et al. 2004).

7.2 Functional Localization of Paravermal and Hemispheric Areas 7.2.1 Rostral and Caudal Lobules The paravermal and hemispheric areas in lobules HIII–HV, HVI, HVIIB, and HVIII (in the human cerebellum; hemispheric lobules III–V, simple lobule, crus II, paramedian lobule, and copula pyramidis are equivalent lobules in the rodent cerebellum; Fig. 6a, black) are both involved in somatosensorimotor control of fine motor activity of body parts in a somatotopic manner (Thickbroom et al. 2003; Manni and Petrosini 2004; Tran-Anh et al. 2020; Fig. 5b). The body area is dually represented in the above two areas in the cerebellum. In the rostral part (lobules III–HVI), the hindlimb, forelimb, and face are represented in more mediorostral, intermediate, and laterocaudal areas, respectively. In the caudal part (lobules HVIIB and VIII), the hindlimb, forelimb, and face are represented in more mediocaudal, intermediate, and laterorostral areas, respectively. Thus, the arrangements of somatotopic representation in the rostral and caudal lobules are more or less in a mirror-image relationship. The somatotopy arrangement in these areas is not as clearly represented as in the cerebral cortex. These lobules receive mossy fiber projections mainly from the pontine nucleus (Biswas et al. 2019), trigeminal nucleus (Welker 1987), dorsal column nuclei, lateral reticular nucleus, and spinal cord in a somatotopic manner. Zebrin-negative and zebrin-faintly positive stripes (equivalent to the C1/C3 module) occupy most of these areas in the paravermal and medial hemispheric parts. Climbing fibers in these areas mainly arise from the dorsal accessory olive (to medial and intermediate parts or the hindlimb and forelimb somatotopy areas) and the dorsomedial subnucleus (to the lateral parts or the face somatotopy area) (Sugihara and Shinoda 2004; Cerminara et al. 2013). The rostral and caudal lobules are topographically innervated by branching climbing fiber axons. The output is relayed mainly by the anterior interpositus nucleus, in a somatotopic manner (hindlimb, forelimb, and face, from the medial to the lateral parts). The rostral and caudal lobules project convergently to the anterior interpositus nucleus (Sugihara and Shinoda 2004; Sugihara et  al. 2009). The anterior interpositus nucleus then projects mainly to the ventrolateral thalamic nucleus and the red nucleus. The

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anterior interpositus nucleus and the cortical areas that are topographically connected to the interpositus nucleus are involved in the control of fine body movements such as grasping, limb cutaneous reflexes, and eyeblink reflex (Ekerot et al. 1997; Pijpers et al. 2008; Horn et al. 2010; Low et al. 2018). Zebrin-positive stripes occupy only a limited extent in these areas (paravermal and hemispheric areas in lobules HIII–HV, HVI, HVIIB, and HVIII). A positive stripe (4+//5+ or C2 module) is present in the paravermal part, and two positive stripes (5+//6+ and 6+//7+ or D1 and D2 modules) are present in the lateral hemispheric parts. Climbing fibers in these areas mainly arise from the rostral part of the medial accessory olive and the principal olive (Sugihara and Shinoda 2004). The output is relayed by the dorsal parts of the posterior interpositus nucleus and the lateral nucleus (Luo et  al. 2017; macrogyric part of the human dentate nucleus, Steele et al. 2017), and projects to the thalamus and other targets. How distinct is the function of these zebrin-positive areas from the function of zebrin-negative and zebrin-faintly positive stripes has not been much clarified. 7.2.2 Medial Paravermal Area of Lobules VI and VII (Lateral A Module) The medial paravermal area of lobules VI and VII (Fig. 6b, black) has climbing fiber and Purkinje cell projections similar to those in the vermis, and is thus designated as the “lateral A module.” This area is more prominent in the rodent cerebellum than in the primate cerebellum (Fujita et al. 2010), and is composed of alternating zebrin-­ positive and zebrin-negative stripes. Developmentally, this area is formed by the lateral migration of Purkinje cell clusters in the central part of the cerebellum (Vibulyaseck et al. 2017). The climbing fibers originate from the medial area of the caudal part of the medial accessory olive. The output of these areas is relayed by the dorsolateral protuberance of the medial nucleus and projects to many targets in the medulla, pons, midbrain, and thalamus (group F1rDLP-“oromotor” and a part of F2-“orienting” of Fujita et al. 2020). 7.2.3 Ansiform Area (Crus I in Rodents, Crus I + II in Primates) Imaging studies in humans have shown that crus I and crus II (or the ansiform area, equivalent to only crus I in the rodent cerebellum) (Fig.  6d, black) are mainly involved in cognitive, executive, language processing, and saccadic functions (Stoodley and Schmahmann 2009; Batson et al. 2015; D’Mello and Stoodley 2015; Guell et al. 2018). Primate studies characterize crus I and crus II by their connectivity to the prefrontal cortex underlying non-motor functions (Strick et al. 2009). In rodents, manipulation of crus I elicits cognitive, execution, and autism-relevant responses (Stoodley et al. 2017; Kelly et al. 2020). These areas receive mossy fiber projections mainly from the rostral, medial, and lateral parts of the pontine nucleus (Biswas et al. 2019). Zebrin-positive stripes occupy most of these areas. Climbing fibers in these areas mainly arise from the medial parts of the caudal parts of the

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medial accessory olive, rostral parts of the medial accessory olive, and rostrolateral parts of the principal olive (Sugihara and Shinoda 2004). These parts of the inferior olive receive projections from the mesodiencephalic junction that mediates the corticofugal projection (Wang et al. 2022). The output is relayed by the ventral parts of the posterior interpositus nucleus and lateral nucleus (Luo et al. 2017; microgyric part of the human dentate nucleus, Steele et al. 2017). The output then has diverse projections to various forebrain areas including the subthalamic nucleus and association cortices (Pisano et al. 2021). 7.2.4 Paraflocculus Human imaging studies showed cognitive function in lobule HIX (e.g., Guell et al. 2018), which is equivalent to the paraflocculus (Fig. 6d, brown). Functional localization of the paraflocculus is not much clarified besides the control of smooth pursuit eye movements in primates (Shidara and Kawano 1993) and tinnitus in rats (Bauer et al. 2013). The possible involvement in non-motor function may be further studied in the animal paraflocculus. The paraflocculus has similar mossy fiber innervation as crus I; it mainly receives projections from the rostral, medial, and lateral parts of the pontine nucleus (Biswas et al. 2019; Na et al. 2019). The whole paraflocculus is zebrin-positive. Climbing fiber projection to this area often comes from branches of the climbing fiber axon projecting to crus I in the rat (Fujita and Sugihara 2013). Climbing fibers in these areas mainly arise from the rostral parts of the medial accessory olive, and rostrolateral parts of the principal olive (Sugihara and Shinoda 2004), which receives projections from the mesodiencephalic junction, which mediate corticofugal projection (Wang et al. 2022). The output is relayed by the posterior interpositus nucleus and dentate nucleus. The output then has diverse projections to various forebrain areas including the subthalamic nucleus and association cortices (Pisano et al. 2021). The overall axonal projection patterns in the paraflocculus have some similarities to those in the ansiform area (crus I in rodents) (Fujita and Sugihara 2013; Biswas et al. 2019). 7.2.5 Flocculus The flocculus (Fig. 6c, black) is involved in the control of reflex eye movements such as vestibuloocular reflex and optokinetic reflex (monkey: Lisberger and Fuchs 1978; cat: Sato and Kawasaki 1984; rabbit: Ito et al. 1977; Barmack et al. 1992; mouse: Koekkoek et al. 1997). The mossy fiber projection to the flocculus mainly comes from the vestibular nuclei (Ando et al. 2020), the primary vestibular afferent, and the contralateral lateral and medial pontine nucleus. The flocculus is composed of three major longitudinally striped areas (Sugihara et al. 2004). The central stripe is involved in horizontal-directional eye movements and receives climbing fiber projections from the dorsal cap. The rostral and caudal stripes are involved in vertical-­directional eye movements and receive climbing fiber projections from the

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ventrolateral outgrowth. The dorsal cap and the ventrolateral outgrowth are neighboring mediodorsal small subnuclei of the inferior olive. These areas in the flocculus project to the most ventral part of the lateral nucleus, dorsal Y nucleus, and some areas in the medial and superior vestibular nuclei. The rostral half of the flocculus has weaker zebrin expression than the caudal half (Fujita et  al. 2014), although matching of the zebrin expression pattern in the flocculus to the three functional longitudinally striped areas is unidentified yet.

8 Concluding Remarks This chapter summarized the macroscopic and histological morphology, axonal connections, and functional localization of the mammalian cerebellum. Although the description is mostly based on findings in rodents, there is a fairly comparative analysis involving human and primate findings. The relationship between the lobular organization and longitudinally striped compartmentalization, and the organization of the olivocorticonuclear topographic connection is complicated. Therefore, we tried to draw a comprehensive general picture rather than making an extensively detailed description in this chapter. Further information is available in Baek et al. (2022) and Pisano et al. (2021) about output projections, Fujita et al. (2020) about the output organization of the medial nucleus, and Luo et al. (2017) and Sugihara (2021) about the ansiform area definition in rodents and primates. Competing Interests  The authors have no competing interests to declare. Acknowledgments  This chapter was supported by Grant-in-Aid for Scientific Research (KAKENHI) from the Japan Society for the Promotion of Science (19K06919 and 21F21107 to I.S., and 19H21208 and 20K15911 to Y.L.). L.Y. is a recipient of the JSPS Postdoctoral Fellowship for Overseas Researchers. O.M.R.N.A. is a recipient of the MEXT Scholarship for Overseas Students.

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Cerebellar Physiology Jasmine Pickford and Richard Apps

Abstract  The cerebellum is typically associated with motor control although there is now extensive evidence that its involvement extends into other domains including cognitive processing. The cerebellum contains a highly regular neural organization, but exactly how this circuitry contributes to its diverse functions remains unclear. Patterns of inputs to and outputs from the cerebellum, together with intracerebellar connections, add layers of complexity to cerebellar computations that can differ between anatomically and physiologically defined modules. Different modules are therefore likely to be specialized for different functions, for example in balance and locomotion versus reach-to-grasp. However, the unifying role of the cerebellum in the control of motor and cognitive behavior may be to serve as a prediction device, refining these predictions based on actual outcomes, to enhance behavioral performance. Keywords  Cerebellum · Purkinje cell · Climbing fiber · Mossy fiber · Cerebellar nuclei · Module · Motor · Learning · Prediction

1 Introduction This chapter aims to provide an overview of the physiology of cerebellar circuits as a framework for the consideration of other chapters in this book. The physiology of the cerebellum has been studied intensively for over a century. A step change in understanding occurred in the 1960s with the pioneering work of Eccles and colleagues, who electrophysiologically characterized the intricate neuronal circuitry of the cerebellum and thus provided a physiological foundation upon which many future studies were based (Eccles et  al. 1967). Given the huge subject area, it is beyond the scope of this chapter to consider all aspects in detail. Instead, the aim is J. Pickford · R. Apps (*) School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_2

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to provide an up to date introduction for those unfamiliar to the topic (and those that welcome a refresher), with a focus on cerebellar physiology in relation to voluntary behavior. This provides the foundations for understanding the etiology of cerebellar disease including the focus of this book, namely ataxia, which is defined as the impaired coordination of voluntary muscle movement (Ashizawa and Xia 2016). The reader is directed to previous reviews for further details (e.g., Middleton and Strick 1998; Garwicz 2002; Llinás et al. 2002; Apps and Garwicz 2005; Ito 2006; Courtemanche et al. 2013; Jörntell 2017; D’Angelo 2018). The current chapter will consider cerebellar physiology in the context of neural circuit loops, including olivo-cortico-nuclear connections, local cerebellar cortical circuits, and reciprocal patterns of input and output connectivity with other brain structures. As a consequence of these circuit loops, rhythmicity and synchronicity appear to be important physiological features of the cerebellum. Evidence is also accumulating to indicate that physiology is non-uniform across cerebellar regions, so caution should be made in assuming the same information transform occurs throughout the cerebellum. The chapter will also explore how physiology translates to behavior, and evidence is presented that the cerebellum acts as a feedforward controller to modulate behavior. Generally speaking, the cerebellum is thought to contain internal models of effector systems that allow it to refine ongoing behaviors without waiting for sensory feedback (Wolpert et al. 1998). As such, the cerebellum is likely to control behavior by generating predictions about future behavioral outcomes that are updated based on the comparison of actual and expected outcomes (Hull 2020). Similar predictive mechanisms may apply across motor and cognitive domains, allowing the cerebellum to optimize the many types of behavior in which it is now known to be involved.

2 Basic Cerebellar Structure 2.1 Gross Cerebellar Structure In order to understand cerebellar physiology, it is necessary to first consider cerebellar anatomical organization. Broadly speaking, the cerebellum has two major subdivisions: the cerebellar cortex and cerebellar nuclei (CN). The cerebellar cortex is highly convoluted and encapsulates the CN, which are situated deep within the cerebellum and are thus often referred to as the deep cerebellar nuclei. From medial to lateral, each half of the cerebellum can be classified into three longitudinal compartments—the vermis, paravermis (intermediate), and hemispheres—and across all compartments the cerebellar cortex has the same basic trilaminar structure, composed from inner to outermost by the granule cell layer, the Purkinje cell (PC) layer, and the molecular layer (Fig. 1). The granule cell layer contains granule cells (the most numerous neuronal cell type in the nervous system), Golgi cells, unipolar brush cells (UBCs), Lugaro cells,

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Fig. 1  Simplified cerebellar circuitry. Inputs to the cerebellum are mainly from mossy fibers and climbing fibers. Mossy fibers synapse onto granule cells (GrCs) that form bifurcating axons known as parallel fibers. Purkinje cells (PCs) receive inputs from parallel fibers and climbing fibers and their main target is the cerebellar nuclei (CN). Other local neurons are also present in the molecular and granule cell layers of the cerebellar cortex. Nucleo-cortical connections, candelabrum cells, and Bergmann glia are not shown. Abbreviations: BC basket cell, GoC Golgi cell, GrC granule cell, IO inferior olive, LC Lugaro cell, SC stellate cell, UBC unipolar brush cell

and a subgroup of Lugaro cells known as globular cells (Lainé and Axelrad 2002). The PC layer contains PCs—the only output neuron of the cerebellar cortex—and candelabrum cells (a type of interneuron: Lainé and Axelrad 1994), as well as Bergmann glia—astrocytes that contribute to cerebellar information processing (De Zeeuw and Hoogland 2015). Finally, the molecular layer contains interneurons including stellate cells and basket cells (Fig. 1). Granule cells and UBCs are excitatory, whereas all the other types of neurons in the cerebellar cortex are thought to be inhibitory. In terms of cerebellar output there are three major subdivisions of the CN located in each half of the cerebellum, from medial to lateral: the medial (fastigial), interpositus (anterior and posterior divisions), and lateral (dentate) nuclei, which receive topographically organized cortico-nuclear inputs from PCs in the overlying vermis, paravermis, and hemispheral cortex, respectively (Voogd 1967; Voogd and Glickstein 1998).

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2.2 Cerebellar Inputs Climbing fibers and mossy fibers form the two major synaptic inputs to the cerebellum and both are excitatory (glutamatergic). In addition, there are several, far less studied, neuromodulatory inputs that project with varying patterns and densities throughout the cerebellum. 2.2.1 Basic Anatomy of Climbing Fiber Projections and Olivo-­Cortico-Nuclear Circuits Climbing fibers originate from a single brainstem nucleus, the inferior olive, which in turn receives widespread inputs from the spinal cord, brainstem, CN, and higher centers including the motor cortex (Llinas et al. 2004). Climbing fibers make direct synaptic contact with cerebellar cortical PCs, and the physiological consequences of this intimate connectivity are discussed later in Sect. 3.1. Several climbing fibers originate from each inferior olive neuron (on average approximately seven per neuron in rats), but each PC is only innervated by a single climbing fiber in the adult rat (Sugihara et al. 1999). The stem axon of individual olive neurons therefore branches to provide climbing fiber input to multiple PCs that are arranged mainly in the rostrocaudal axis (Apps and Garwicz 2005). On their path to the cerebellar cortex, olivary axons also form collateral inputs onto CN neurons (Fig. 1), typically forming between one and six collaterals terminating in a particular nucleus (Sugihara et al. 1999). Small populations of neurons located within different subnuclei of the inferior olive give rise to climbing fibers that target a specific rostrocaudally orientated “zone” of PCs in the cerebellar cortex (Fig.  2, Apps and Garwicz 2005). These zones can be identified both anatomically and physiologically, with each zone typically one to three millimeters in mediolateral width but extending for many millimeters in the rostrocaudal axis (Apps and Hawkes 2009). The PCs in each cortical zone provide a convergent cortico-nuclear inhibitory projection to neurons in a specific region of the CN, thereby forming multiple, olivo-cortico-nuclear connections termed “modules” (Fig. 2, Apps and Garwicz 2005; Apps and Hawkes 2009). This modular organization extends to nucleo-olivary projections arising from the same region of CN that provides inhibitory feedback to the originating olivary subnucleus via GABAergic projections (Fig. 2). As a result, PCs can influence their own climbing fiber inputs by modulating the inferior olive neurons from which the climbing fibers arise, via CN neurons (as shown in paravermal regions by Chaumont et al. 2013). Individual cerebellar modules are thought to subserve different functions (Horn et al. 2010, although see Cerminara and Apps 2011). For example, the vermal A module is associated with head movements, balance, and postural control; the paravermal C modules are involved in limb movements, including grasping; and the lateral D2 module is involved in predicting target motion during visually guided movements (Fig.  2c; Cerminara and Apps 2011). It is important, however, to

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Fig. 2  Cerebellar zones and modules. (a) Olivo-cortico-nuclear circuit within the cerebellar circuitry (see Fig. 1 for abbreviations); (b) Purkinje cells (PCs) receiving common inferior olive (IO) climbing fiber inputs form a “zone,” and these PCs together with their IO input and cerebellar nuclei (CN) output regions form a “module”; (c) Modules defined in rats in each half of the cerebellum from medial to lateral

emphasize that individual modules are not likely to be restricted to specific functions, not least because of the various interactions that can occur between them (see below). The cortical component of some modules can be further divided into microzones that contain PCs with similar climbing fiber receptive fields (e.g., Andersson and Oscarsson 1978; Ekerot et al. 1991). Microzones and their microcomplex connections with the CN and inferior olive are thought to represent the basic functional units of the cerebellum (Apps and Hawkes 2009). 2.2.2 Basic Anatomy of Mossy Fiber Projections In stark contrast to the climbing fiber system, mossy fibers arise from multiple brain nuclei distributed throughout the central nervous system, including all segmental levels of the spinal cord, numerous brainstem nuclei, but most notably the basilar pontine nuclei (which receive inputs primarily from the neocortex; Llinas et  al. 2004). Mossy fibers branch widely in the cerebellar cortex, usually in the rostrocaudal dimension, and in the rat follow a similar pattern of termination as climbing fibers in the overlying molecular layer, although their organization is less precise (Voogd et al. 2003; Pijpers et al. 2006). This suggests that broadly speaking, mossy fiber termination patterns adhere to the modular organization of the olivo-cortico-­ nuclear system, and that these projections are targeted to certain functions rather than forming a diffuse, generalized input. However, given that mossy fibers have collaterals in the mediolateral plane they may be able to influence multiple modules (Shinoda et al. 1992; Wu et al. 1999), and mossy fiber projections may also vary within a given module as demonstrated in the C1 zone of the rat (Herrero et  al. 2002, 2012).

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Mossy fibers synapse onto granule cells in the granule cell layer and many also form excitatory collateral inputs to CN neurons on their route to the cortex (Fig. 1). Mossy fiber collaterals arising from a given axon may target different divisions of the CN, again suggesting that mossy fiber inputs are not universally aligned with climbing fiber inputs to the cerebellum (Wu et al. 1999). Granule cell axons bifurcate to form parallel fibers that contact many PCs in the long axis of individual cerebellar folia and so multiple cerebellar modules can be connected via common parallel fiber inputs (Valera et al. 2016; Binda et al. 2016). A subset of mossy fibers (approximately 5% in cat) arise from the CN and provide a nucleo-cortical feedback projection to the cerebellar cortex, targeting the granule cell layer (Houck and Person 2014). While some of these neurons target areas of the cerebellar cortex that provide reciprocal PC cortico-nuclear projections, others target regions of the cortex from which they receive no input, thereby forming an additional route for modules to interconnect (Trott et al. 1998a, b). In mice, a proportion of the nucleo-cortical connections arise from collaterals of the large glutamatergic projection neurons in the CN (Houck and Person 2015) and are thought to act as an internal amplification system to assist associative learning (Gao et al. 2016). In addition, a subpopulation of nucleo-cortical neurons, also described in mice, are inhibitory and target Golgi cells in the granule cell layer, thereby allowing disinhibition of cerebellar cortical circuits (Ankri et al. 2015). 2.2.3 Neuromodulatory Inputs As well as climbing fiber and mossy fiber glutamatergic inputs, neuromodulatory afferents targeting the cerebellum release either serotonin, noradrenaline, acetylcholine, dopamine, or histamine (Schweighofer et al. 2004). These inputs differ in their pattern of termination and are not uniformly distributed throughout the cerebellum. Far less is known about their physiology but they may have roles in regulating cerebellar development (Oostland and van Hooft 2013), synaptic transmission and plasticity (Lippiello et al. 2015), and modifying cerebellar activity throughout different stages of the sleep–wake cycle (Brown et al. 2001; Jaarsma et al. 1997).

2.3 Non-uniformity in Cerebellar Anatomy The preceding sections outline the classical, orderly microcircuit organization of the cerebellum, characterized by olivo-cortico-nuclear loops. However, it is becoming increasingly clear that there are also important regional variations in anatomy that confer differences in physiological properties. In particular, it has long been known that a variety of molecular markers are differentially expressed throughout the cerebellar cortex, providing anatomical and physiological subdivisions. Most notable among these markers is zebrin II (also known as aldolase C), which in some regions of the cerebellar cortex is expressed in subsets of PCs forming a highly

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characteristic and reproducible pattern of stripes, with alternating positive and negative rostrocaudally oriented bands of expression. Zebrin II colocalizes with several other markers, such as phospholipase Cβ3, excitatory amino acid transporter 4 (EAAT4), and metabotropic glutamate receptor 1a (mGluR1a), while some markers are present only in zebrin II-negative PCs, such as phospholipase Cβ4. This pattern of protein distribution appears to be present in the cerebellum of all birds and mammals, including humans, and in some regions of the cerebellar cortex has been found to correspond to the organization of both mossy fiber and climbing fiber inputs (Apps and Hawkes 2009). This relationship between molecular signature and anatomical circuits extends to PC cortico-nuclear projections, suggesting a common spatial organization of cerebellar cortical inputs, PC phenotype, and cortico-nuclear outputs (Apps and Hawkes 2009). Another important example of non-uniformity is the distribution of UBC cerebellar cortical interneurons. These are glutamatergic, located in the granule cell layer (Fig. 1), and are found mainly in the vermis and flocculonodular lobe where they provide feedforward amplification of cerebellar inputs. Since these regions of the cerebellum are known to be involved in the regulation of body, head, and eye position, UBCs are thought to serve a specific cellular function within these cerebellar regions relating to these behaviors (Mugnaini et al. 2011). In mice, PC collaterals to UBCs preferentially inhibit UBCs expressing metabotropic glutamate receptor 1 (mGluR1), adding an extra level of heterogeneity even within regions containing UBCs (Guo et al. 2021).

3 Cellular Physiology 3.1 Cortical Circuits 3.1.1 Inputs to the Granule Cell Layer Granule cells account for over half of all neurons in the human brain (Herculano-­ Houzel 2010). Their primary input is from mossy fibers, which terminate in structures called glomeruli, with an average of four glutamatergic mossy fiber inputs to each granule cell (Eccles et al. 1967). The structure of a glomerulus allows glutamate released from one mossy fiber terminal to spillover onto neighboring granule cell dendrites within the glomerulus, which may improve efficacy of neurotransmission (DiGregorio et al. 2002). Transmission at mossy fiber–granule cell synapses is thought to be highly secure, with stimulation of a single mossy fiber at high frequencies evoking granule cell firing in vivo (Rancz et al. 2007). Other studies, however, suggest that synchronous input from multiple mossy fibers is required to evoke granule cell firing, and that subthreshold signals are filtered out (Jörntell and Ekerot 2006). These differences in synaptic efficacy may be the result of a number of possibilities, including regional variations (see Sect. 2.3) and the nature of the inputs being encoded.

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Mossy fibers transmit sensorimotor, proprioceptive, and contextual information, with inputs from different body parts represented in different cerebellar regions in line with the somatotopic organization of the cerebellum (see Sect. 4.1; Arenz et al. 2009). Granule cells may receive input from multiple modalities; for example vestibular, visual, and eye-movement-related signals converge on individual granule cells in the flocculus of mice (Arenz et  al. 2008). This means that granule cells transmit distinct outputs that depend on the specific combination of inputs they receive rather than acting as a simple relay, thereby enriching sensory representations for further cerebellar processing (Chabrol et al. 2015). The granule cell layer is thought to facilitate pattern separation through the connections of single mossy fibers to many granule cells, and their output is passed to PCs via parallel fibers (granule cell axons). Granule cells have also been shown to encode non-sensorimotor, predictive information. They are able to encode the expectation of reward (Wagner et al. 2017), and also encode an acquired conditional response following eyeblink conditioning training (Giovannucci et al. 2017). This suggests that prediction is apparent even at the input stages of information processing within the cerebellar cortex. Golgi cells, found in the granule cell layer, provide inhibition to granule cells and receive excitatory input from both mossy fibers and parallel fibers (Fig. 1). Golgi cells therefore provide both feedforward inhibition (as a result of their mossy fiber inputs) and feedback inhibition (via granule cell axon and parallel fiber inputs) to their target granule cells (D’Angelo 2008). 3.1.2 Parallel Fiber Inputs to the Molecular Layer Granule cell axons bifurcate to form parallel fibers that extend along the molecular layer (Fig.  1, Sect. 2.2.2). Parallel fibers are slowly conducting but  evoke rapid excitatory responses in all neurons in the molecular layer, including PCs, molecular layer interneurons (MLIs), and the dendrites of Golgi cells (Jirenhed et al. 2013). Owing to the large number of granule cells, each PC is estimated to be contacted by around 150,000 parallel fiber synapses (Zang and De Schutter 2019), although many of these may be functionally weak or silent (Isope and Barbour 2002). The parallel fiber–PC synapse is an important site of plasticity, which is discussed further in Sect. 3.4.1. Parallel fiber inputs to MLIs provide feedforward inhibition onto PCs, which can modulate the efficacy of parallel fiber inputs (Binda et al. 2016). Maintenance of the excitatory–inhibitory balance of PC inputs is important for cerebellar functioning, given silencing MLIs’ changed firing patterns of PCs, increasing simple spike rate and regularity, and impaired locomotor behavior (Jelitai et al. 2016). Basket cells and stellate cells are both subtypes of MLIs, and they may have different impacts on PC signaling. Removing GABAergic transmission from basket cells of behaving mice was shown to increase simple spike rate in PCs while decreasing complex spike rate; the same manipulation in stellate cells increased the regularity of simple spikes and increased complex spike rate (Brown et al. 2019). Therefore, MLI subtypes are likely to have different roles in cerebellar information processing.

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3.1.3 Purkinje Cell Simple Spikes and Complex Spikes Climbing fibers provide an incredibly powerful excitatory synaptic input to PCs and as a result generate unique action potentials known as complex spikes (Fig.  3; Eccles et al. 1966; Thach 1967). While the simple spikes generated by a PC resemble typical action potentials (duration one to two milliseconds), complex spikes generated by the same PCs are longer in duration (approximately 10 milliseconds on average) and consist of an initial large sodium-dependent component followed by a variable number of smaller, calcium-dependent components known as spikelets (Fig. 3; Eccles et al. 1966). Simple spikes are generated both intrinsically and as a result of mossy fiber–parallel fiber inputs, and occur at a wide range of firing frequencies, ranging from 19 to 95 Hz in vivo (mean 44 Hz; Armstrong and Rawson 1979) and 1 to 148 Hz (mean 38.8 ± 2.4 Hz) in vitro, even in the absence of synaptic inputs (Hausser and Clark 1997). By contrast, complex spikes arise solely as the result of climbing fiber input. They occur at approximately 1 Hz in the awake animal, and their occurrence causes a subsequent pause in simple spike firing, the duration of which may relate to the number of spikelets in the complex spike (Fig. 3; Burroughs et al. 2017). The secondary components of a complex spike can reach frequencies of 500–600  Hz (Campbell and Hesslow 1986) and vary considerably in number, but typically each complex spike has three or four spikelets (Burroughs et al. 2017). MLIs, which form inhibitory connections with PCs (Fig. 1), have been shown in rat cerebellar slices to Fig. 3  An example extracellular recording from a single Purkinje cell showing complex spikes and simple spikes. The number of spikelets within a complex spike is variable, as shown in the insets. (Modified from Burroughs et al. 2017)

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also receive inputs from climbing fibers as a result of glutamate spillover (Szapiro and Barbour 2007), so as well as providing direct excitatory inputs to PCs, climbing fiber inputs may also produce feedforward inhibition. Inferior olive neurons, the origin of climbing fibers, are electrotonically coupled via gap junctions that synchronize their activity (Leznik and Llinás 2005). Synchronous activation of climbing fibers is thought to be important for the initiation and coordination of movement in mice (Hoogland et  al. 2015), and greater complex spike synchrony increases the amplitude of complex spike-induced short-­ latency inhibitory and long-latency excitatory responses in CN neurons of anesthetized rats (Tang et al. 2019). Coupled PCs, as determined by correlations in complex spike occurrence, also have increased likelihood of simple spike synchrony (Wise et al. 2010). In addition, the number of spikelets in a complex spike correlates with synchronization (Lang et al. 2014), and the variability in spikelet number suggests that complex spikes are not an “all-or-nothing” event but instead convey information (Zang and De Schutter 2019). Thus, the complex spike activity of PCs, particularly when synchronized, can result either directly or indirectly in changes in cerebellar output, which in turn has the potential to influence behavior. The role of complex spikes and simple spikes in behavior is further discussed in Sect. 5.3.4. 3.1.4 Purkinje Cell Targets Within the Cerebellar Cortex In mice, PCs have been found to directly inhibit other cell types in the cerebellar cortex via axon collaterals in the parasagittal plane, including neighboring PCs, MLIs, and Lugaro cells (Witter et al. 2016), thereby regulating their own inputs. In terms of cerebellar cortical non-uniformity, there may also be regional differences in how PCs regulate their own feedback. For example, PC axon collaterals directly inhibit granule cells in localized regions of the cerebellum related to eye movements and vestibular processing (lobule X, ventral paraflocculus, and flocculus; Guo et al. 2016). As well as these local synaptic connections, studies in mice have also shown that climbing fiber synapses to one PC can generate large negative extracellular signals that suppress simple spikes in neighboring PCs via ephaptic coupling (Han et al. 2020). This means that a single climbing fiber may in fact influence multiple local PCs, which may, in turn, promote firing of cerebellar output neurons due to synchronous disinhibition. Connections between cells of the same type are a common feature in the cerebellum. As well as PCs targeting other PCs as described above, the same principle also holds true for cerebellar cortical interneurons. MLIs and Golgi cells are connected to the same cell type by both electrical (gap junction) and chemical (GABAergic) synapses (Mann-Metzer and Yarom 1999; Rieubland et al. 2014)—the former promoting synchronization in Golgi cells (Dugué et al. 2009). The extent to which this reciprocity is important for cerebellar function requires further investigation.

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3.2 Purkinje Cell Control of Cerebellar Nuclei PCs are the sole output of the cerebellar cortex, projecting to either the CN or vestibular nuclei, and are therefore central to cerebellar information processing. They are inhibitory, using GABA as a neurotransmitter, and form the main synaptic input to CN neurons with approximately 60% of synaptic boutons in the CN arising from PC axons (Ito 1984). Other synaptic inputs to CN are mainly from glutamatergic climbing fiber and mossy fiber collaterals (Fig. 1). In mice, excitatory climbing fiber collateral inputs to CN neurons resulting from olivary stimulation have been shown to be overridden by climbing fiber-induced inhibition via the PC pathway (Lu et al. 2016), suggesting that PC inhibition is the dominant CN input. PC complex spikes in vivo have been shown to exert a strong and long-lasting inhibitory effect on activity in some CN neurons, although in others there is an excitatory–inhibitory sequence. Andersson and Oscarsson (1978), who first identified microzones in the B zone of the cat cerebellum, showed that lateral vestibular nuclear neurons were activated by collaterals of climbing fibers projecting to PCs providing inhibition to the same group of neurons. Therefore the same climbing fiber axons can produce excitation of CN neurons via direct collateral inputs followed by indirect inhibition via PC inputs (Blenkinsop and Lang 2011). In young rat cerebellar slice preparations, hyperpolarizing currents (mimicking PC inhibition) are able to reduce spontaneous activity of CN neurons and subsequently elicit a rebound depolarization (Aizenman and Linden 1999). In mice this depolarization is thought to underlie rebound increases in CN firing rate that occur in vivo following trains of stimuli delivered to the cerebellar cortex or the inferior olive (Hoebeek et al. 2010). Similarly, in cat, synchronous climbing fiber activation evoked by electrically stimulating the peripheral receptive field results in substantial inhibition of CN neurons followed in some cases by rebound responses, suggesting this may be an important feature of the olivo-cortico-nuclear system (Bengtsson et al. 2011). However, it remains a matter of debate whether rebound firing occurs under physiological conditions because it has been less reliably observed in vivo as compared to in vitro investigations, and often involves non-physiological patterns of stimulation (Alvina et al. 2008; Witter et al. 2013). Other studies argue that asynchronous PC inputs suppress CN firing while synchronous activity can entrain nuclear firing to PC inputs (Person and Raman 2012a, b), with single stimuli to the cerebellar cortex in rodents evoking precisely timed action potentials without changing firing rate (Hoebeek et al. 2010). The net effect of PC inhibition on individual CN neurons therefore likely depends on the degree of PC synchrony together with the level of PC–CN convergence (Tang et al. 2016). Further study is required to clarify how these factors contribute to cerebellar functions, and to determine if the mechanisms of PC to CN signaling are consistent throughout the cerebellum. The nature of PC influence on the CN is also complicated by the presence of multiple cell types within the CN, including: (1) large glutamatergic neurons projecting to extracerebellar targets to provide powerful and short-latency excitatory

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connectivity, notably with the thalamus and red nucleus, to influence motor and premotor areas; (2) small GABAergic projection neurons, which are the origin of the topographically organized nucleo-olivary inhibitory projection mentioned above (Uusisaari and Knopfel 2011); (3) glycinergic premotor output neurons in the medial nucleus (Bagnall et al. 2009); (4) inhibitory projection neurons of the interpositus nucleus, which have been recently described in mice, with inputs to regions including the pontine nuclei, medullary reticular nuclei, and sensory brainstem structures, such as the external cuneate nucleus, cuneate nucleus, parabrachial nuclei, and vestibular nuclei (Judd et al. 2021); (5) a heterogeneous population of local interneurons including an inhibitory population with a mixed GABAergic and glycinergic phenotype (Husson et al. 2014), and a non-GABAergic (putatively glutamatergic) population (Uusisaari and Knopfel 2012); and (6) nucleo-cortical neurons, which project to the cerebellar cortex and can be inhibitory or excitatory as outlined in Sect. 2.2.2 (Ankri et al. 2015; Houck and Person 2014). There is evidence that the effects of PC inhibition on CN neurons may depend on cell type. In vivo recordings in mice suggest that glutamatergic cells of the medial nucleus respond to the rate and timing of PC inputs, with synchronous PC activation entraining CN neuron activity, whereas GABAergic neurons respond to mean population firing rates and may therefore encode PC inhibition differently (Özcan et al. 2020). A key outstanding question is how different cell types across the nuclei respond to their synaptic inputs, and how they interact with one another to shape CN output.

3.3 Zebrin Stripes Several important physiological differences have been found in  vivo in rodents between zebrin II positive (Z+) and negative (Z–) PCs (see Sect. 2.3). Firstly, simple spike firing rates are higher on average in Z– PCs than Z+ PCs (Zhou et al. 2014; Xiao et al. 2014). Secondly, the climbing fiber-evoked pause in simple spike firing is shorter in duration in Z– PCs (Xiao et al. 2014). And thirdly, the regularity of simple spike firing rates is greater in Z– PCs (Zhou et al. 2014; Xiao et al. 2014). These systematic differences in simple spike activity are thought to be due to the presence of the transient receptor potential cation channel C3 (TRPC3) in Z– PCs, since blocking these channels pharmacologically reduces simple spike firing rates in Z– but not Z+ PCs (Wu et al. 2019). Mice with loss of TRPC3 function show impaired eyeblink conditioning, a form of cerebellar learning that occurs in cerebellar cortical regions associated with Z– PCs, whereas compensatory eye movement adaptation, related to Z+ regions, remains intact (Wu et  al. 2019). This suggests important differences in function of Z+ and Z– PCs, reinforcing the notion that cerebellar cortical physiology is not uniform. Complex spike firing rates are also higher on average in Z– PCs than in Z+ PCs. Moreover, complex spikes have a longer half-width, implying a larger number of spikelets, and larger spike area in Z+ PCs (Zhou et al. 2014). Z+ PCs in vitro display

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prolonged excitation following climbing fiber activation due to terminals in Z+ regions having larger pools of release-ready vesicles and enhanced multi-vesicular release, thus triggering longer-duration complex spikes with a greater number of spikelets (Paukert et al. 2010). However, the same phenomenon was not observed in vivo with Z+ and Z– PCs having similar distributions of spikelet number (Tang et al. 2017). Z+ PCs also show a greater variety of simple spike responses following a complex spike (Zhou et al. 2014), which may be related to differences in mossy fiber–granule cell–parallel fiber inputs, MLI inputs, and/or differences in PC intrinsic excitability. The systematic differences in zebrin expression in the cerebellar cortex are also retained in some regions of the CN. The lateral, posterior interpositus and caudal medial nuclei contain terminals of Z+ PCs, whereas the anterior interpositus and rostral medial nucleus receive Z– PC terminations (Sugihara 2011). It might be expected that by comparison to Z+ PCs, Z– PCs cause more inhibition in their target CN neurons due to their higher firing rates and therefore Z– PC targets would have lower firing rates; however, recent research suggests the opposite. In awake adult mice, firing rates are consistently lower in CN neurons receiving input from Z+ PCs than those with input from Z– PCs (Beekhof et al. 2021). Identifying the reason(s) for this difference could enhance our understanding of how information is processed in cerebellar modules related to different behaviors. In addition to Z+ and Z– PCs having distinct physiology, there is also evidence that zebrin stripes can act together in functional pairs; in the pigeon vestibulo-­ cerebellum, pairs of Z+ and Z– bands form functional units in relation to patterns of optic flow, e.g., self-rotation about the vertical axis (Graham and Wylie 2012). It remains to be determined exactly how zebrin II-related differences in physiology relate to differences in output, and ultimately function, throughout the cerebellum.

3.4 Synaptic Plasticity 3.4.1 Parallel Fiber–Purkinje Cell Synaptic Plasticity Plasticity in the cerebellum was first studied at the parallel fiber–PC synapse, where long-term depression (LTD) was found to be induced by paired stimulation of parallel fibers and climbing fibers in vitro (Marr 1969; Albus 1971; Ito and Kano 1982). Ito et al. (1982) showed that LTD at this synapse could be induced in vivo by coincident stimulation of the sources of mossy fibers and climbing fibers, the vestibular nerve and inferior olive respectively, to the flocculus in decerebrate rabbits. The mechanisms of LTD are described in detail in a review by Hoxha et  al. (2016). In brief, parallel fiber–PC LTD requires postsynaptic calcium influx resulting from climbing fiber input together with intracellular release of calcium resulting from activation of mGluR1 by glutamate released from parallel fibers (which also activates AMPA receptors). The increase in intracellular calcium leads, via a biochemical cascade involving protein kinase C, in the endocytosis of postsynaptic AMPA recptors in PCs. This renders the PC less responsive to parallel fiber inputs.

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Long-term potentiation (LTP) can also be induced at this synapse by stimulation of parallel fibers alone, typically at 1 Hz, which leads to insertion of AMPA receptors into the postsynaptic membrane (Salin et al. 1996; Lev-Ram et al. 2002). This type of stimulation results in relatively low levels of intracellular calcium compared to the LTD protocol described above, promoting activation of protein phosphatases and binding of N-ethylmaleimide-sensitive factor to AMPA receptors, leading to the stabilization of these receptors in the postsynaptic membrane (Hoxha et al. 2016). In fact, both LTD and LTP can occur at most synapses within the cerebellum through a variety of mechanisms (for reviews, see Mapelli et al. 2015; Gao et al. 2012). Parallel fiber–PC synaptic transmission is dysfunctional in rodent models of spinocerebellar ataxia, for example because of abnormal regulation of mGluR1 (SCA1, SCA5) or deficient release of glutamate from parallel fibers (SCA27, Hoxha et al. 2016). Physiological functioning of this synapse is therefore central to normal cerebellar function and genetic causes of ataxia may lead to its dysregulation. The roles of parallel fiber–PC plasticity in behavior are further explored in Sect. 5.3.1. 3.4.2 Zebrin II and Synaptic Plasticity LTD is thought to be the predominant form of parallel fiber–PC plasticity in regions of cortex containing Z– PCs because of their relatively high baseline firing rate, while for Z+ PCs their lower firing rate predisposes them to LTP (De Zeeuw and Ten Brinke 2015; De Zeeuw 2021). Zebrin II colocalizes with EAAT4, a transporter that limits the duration of action of glutamate at the synapse, and this reduction of glutamate prevents LTD by reducing activation of metabotropic glutamate receptors. Consistent with this transmitter regulation, studies in rat cerebellar slices, in which parallel fiber stimulation was paired with PC depolarization, induced LTD of parallel fiber inputs to Z– PCs (in lobule III), but under the same conditions did not elicit plasticity at parallel fiber inputs to Z+ PCs (in lobule X; Wadiche and Jahr 2005). EAAT4 is also likely to influence other cerebellar cortical signaling, as it regulates glutamate spillover from climbing fibers to MLIs (Malhotra et al. 2021). Such findings therefore strongly suggest that the same synaptic transmission and plasticity rules are not likely to apply to the cerebellum as a whole. 3.4.3 Plasticity at Cerebellar Nuclei Synapses Studies in mice in vitro have shown that LTP can be induced at the mossy fiber–CN synapse by high-frequency stimulation of mossy fibers combined with hyperpolarization of the postsynaptic CN neuron, which in turn leads to rebound firing (Person and Raman 2010; Pugh and Raman 2006). By contrast, LTD can be induced at the same synapse via high-frequency stimulation either with or without depolarization of the postsynaptic CN neuron (Zhang and Linden 2006). LTP (Ouardouz and Sastry 2000) and LTD (Morishita and Sastry 1996) can also be induced at PC inputs to CN neurons. Indeed, one study has shown that a particular burst protocol can

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induce both LTP and LTD at PC synapses onto CN neurons in rat cerebellar slices, depending upon the level of postsynaptic excitation (Aizenman et  al. 1998). The direction of plasticity (LTP versus LTD) depends on the state of the postsynaptic CN neuron, which suggests that the level of inhibition from PCs (and potentially local interneurons) regulates plasticity of excitatory synapses to CN neurons. Such an arrangement could be a homeostatic mechanism to maintain synaptic strength within an operational range. As both LTP and LTD can occur at various nodes within the cerebellar circuitry, this creates a high level of complexity that goes beyond the classical view of LTD at the parallel fiber–PC synapse being the key mechanism of cerebellar synaptic plasticity (for more detail, see Sect. 5.3.1). The balance of increases and decreases in plasticity, which lead to changes in cerebellar output, depends upon the timing of synaptic inputs, the state of excitability of the neurons, and the cerebellar region of interest. Little is known about how different forms of plasticity interact with one another to influence cerebellar information processing.

4 Systems Physiology 4.1 Somatotopic Organization Central to cerebellar function, particularly for its contributions to motor control, is how the cerebellum receives sensory inputs from the body and sense organs. The first systematic report of a somatotopic organization in the cerebellum was by Snider and Stowell (1944) who recorded, in the anesthetized cat and monkey, field potentials on the cerebellar surface evoked by peripheral tactile stimulation, and observed responses in discrete regions of the cerebellar cortex that were organized in a similar pattern in both species—one map in the anterior lobe and two more in the posterior lobe of the cerebellum. A similar somatotopic organization has subsequently been described in a range of other species, notably the rat (Atkins and Apps 1997; Jörntell et al. 2000), and fine-resolution mapping has shown that this somatotopy corresponds to cerebellar cortical zones (Sect. 2.2.1). More recently, non-­ invasive functional magnetic resonance imaging (fMRI) has shown, albeit at a coarser level of spatial resolution, that the same general somatotopic arrangement is also present in the human cerebellum (Fig. 4, Grodd et al. 2001; Ashida et al. 2019; Boillat et al. 2020). A basic somatotopy also exists within the CN, and therefore in the cerebellar output. For example, in cats and monkeys the fore- and hindlimbs are represented in posterior and anterior regions, respectively, of the anterior interpositus (van Kan et al. 1993; Garwicz and Ekerot 1994), while in dentate both face and eyes are represented (van Kan et al. 1993). However, in other CN regions a somatotopy is not evident. For example, in the posterior interpositus in monkeys there is no clear separation between representation of the fore- and hindlimbs (van Kan et al. 1993).

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Fig. 4  Somatotopic organization in a dorsal view of the rat and human cerebellar cortex. Left shows approximate locations of representations of the hindlimb, forelimb, and face in the rat based on works by Atkins and Apps et al. (1997), Jörntell et al. (2000), and Bosman et al. (2010), determined by electrophysiological responses to peripheral stimulation. Right shows approximate representations of the foot/toes, hand/digits, tongue, eyes, and lips as described by Boillat et al. (2020) and Grodd et  al. (2001), determined using fMRI in participants voluntarily moving specified body parts

The somatotopy within the CN has been studied in most detail in the anterior interpositus in the cat and a finer map of the ipsilateral forelimb is present that relates to the olivo-cortico-nuclear projections of the paravermal C1, C3, and Y modules (Garwicz and Ekerot 1994). Ekerot et al. (1995) found that microstimulation of these different CN regions in cat elicited different patterns of multi-­segmental ipsilateral forelimb movement. This suggests that, at least for paravermal regions, the cerebellar control of movements is organized in a modular framework. Consistent with this possibility, recent research in mice has shown that a small area in the rostral part of the anterior interpositus controls motor synergies that protect the eye during eyeblink conditioning by coordinating the eyelid, neck, and forelimb muscles, and that individual CN neurons encode information related to all these effectors (Heiney et al. 2021). This provides support to the general concept that cerebellar maps are related more to actions of different body parts rather than being strictly somatosensory (Apps and Garwicz 2005). As outlined above (Sect. 2.2.2), anatomical studies have provided evidence that mossy fibers generally align with the climbing fiber organization in the cerebellum. However, detailed electrophysiological mapping of multiunit granule cell activity driven by mossy fiber inputs in anesthetized rats has also revealed a “fractured somatotopy” of tactile responses whereby different body parts are represented in a patchy mosaic pattern in the hemispheres of the cerebellar cortex (Shambes et al. 1978; Kassel et  al. 1984; Apps and Hawkes 2009). The apparent discrepancy

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between a fractured and a more systematic somatotopic arrangement could be the result of several possibilities including regional differences in cerebellar physiology (paravermal versus hemispheral cortex), and the extent to which local granule cells have their main influence on overlying PCs or on PCs in other regions of cortex via their parallel fibers. Further studies are required to investigate these and other possibilities. In summary, the somatotopic organization of inputs to and outputs from the cerebellum suggests that the physiological organization of cerebellar connectivity is highly conserved across species, and that output of different cerebellar regions is likely to subserve control of coordinated movements that may involve a combination of different body parts. However, the way CN interact with the rest of the central nervous system to coordinate complex movements remains poorly understood.

4.2 Physiologically Defined Olivocerebellar Pathways Electrophysiological studies have revealed a complex array of spino-olivocerebellar pathways (SOCPs, Fig. 5) that transmit information from skin, muscle, and joints to the inferior olive, which in turn forward this information to the cerebellum via climbing fibers (Oscarsson 1980; Ito 1984). Transmission in SOCPs can be recorded as evoked climbing fiber field potentials in the cerebellum, and combined electrophysiological mapping and anatomical tract tracing experiments have shown that cerebellar cortical zones defined by their SOCP input and those defined anatomically by their olivo-cortico-nuclear connectivity are largely congruent (e.g., Trott and Armstrong 1987a, b; Trott and Apps 1991, 1993; Edge et al. 2003). A similar arrangement occurs for descending inputs from the cerebral cortex, which collectively are termed cerebro-olivocerebellar pathways (COCPs, Fig. 5). COCPs originate in many regions of the cerebral cortex and project to the inferior olive via the mesodiencephalic junction (Wang et al. 2022). COCPs also conform to the zonal organization of the cerebellum; they converge on the same olivary regions that supply climbing fibers to the cortical zones defined by the SOCPs (Andersson and Nyquist 1983). For example, stimulation of the ipsilateral forelimb and the somatotopically corresponding region of the contralateral motor cortex results in convergent cerebellar climbing fiber responses in the forelimb-receiving part of the C1 zone in the rat (Ackerley et al. 2006). The source of climbing fibers, the inferior olive, receives a variety of sensory and motor inputs from regions including the trigeminal nuclei, dorsal column nuclei, red nuclei, cerebral cortex (via the mesodiencaphalic junction, as described above), CN, and spinal cord (De Gruijl et al. 2013). This convergence of inputs, together with the fact that COCPs conform to the zonal organization of the olivocerebellar system, reinforces the close relationship between ascending and descending pathways to the cerebellum and highlights the potential role of the inferior olive as a comparator of these two sources of information (Oscarsson 1980; for further discussion, see Sect. 6).

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Fig. 5  Simplified diagram of cerebellar input pathways as depicted on a rat brain and spinal cord. Spino-olivocerebellar pathways (SOCPs) carry information from the spinal cord to the inferior olive (IO), which send climbing fiber inputs to the cerebellum. These ascending pathways include both direct spino-olivary projections and indirect pathways via various brainstem relays including the dorsal column nuclei. Cerebro-olivocerebellar pathways (COCPs) also provide climbing fiber input to the cerebellum via the inferior olive but originate in the cerebral cortex and include descending pathways that relay in a range of brainstem nuclei including the midbrain tectum. Mossy fibers include both direct and indirect projections from the spinal cord (spinocerebellar pathway, SCP), and indirect projections from the cerebral cortex via the pontine nuclei (Pn) in the cerebro-pontocerebellar pathway (CPCP)

The flow of information via SOCPs and COCPs is modulated during active movements. In particular, a series of studies in awake behaving cats has shown that separate modulatory drives act on the climbing fiber pathways that target the paravermal zones (Apps et al. 1990, 1995, 1997; Lidierth and Apps 1990; Apps and Lee 1999; Pardoe et al. 2004). For example, low-intensity electrical stimulation of the ipsilateral superficial radial nerve in cats evokes SOCP-mediated field potentials in the cerebellar cortex, which vary systematically in size throughout the step cycle. By comparison to rest, responses recorded in the C2 zone were usually smallest (implying reduced SOCP transmission) in the swing phase of the step cycle in the ipsilateral forelimb (Apps et al. 1990). In contrast, in the neighboring C1 zone, the smallest responses consistently occurred during the stance phase in the ipsilateral forelimb (Lidierth and Apps 1990). Such differences suggest functional variations between zones, and it is thought that the gating of sensory inputs serves to prevent the transmission of self-generated, predictable signals in a task-dependent manner (Lawrenson et al. 2016).

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4.3 Spinocerebellar Mossy Fibers Cerebellar mossy fibers arise via multiple pathways including spinocerebellar, cuneocerebellar, reticulocerebellar, and cortico-pontocerebellar tracts (Fig.  5). Spinocerebellar pathways provide proprioceptive information from skin, muscle, and joints to the cerebellum (Bloedel and Burton 1970; Snyder et al. 1978), but may encode information about movement of a body region, e.g., a whole limb rather than individual joints or muscles (Bosco and Poppele 2001). The importance of these pathways is emphasized by the fact that they include some of the fastest conducting axons in the central nervous system, with degeneration of spinocerebellar tracts resulting in profound disorders to movement control, characterized by Friedreich’s ataxia.

4.4 Cerebro-Cerebellar Pathways The cerebellum receives substantial inputs from the cerebral cortex mainly via pontine nuclei (cerebro-pontocerebellar pathways; Fig.  5) and the inferior olive (COCPs). The cerebellum also sends projections to the cerebral cortex, predominantly via the thalamus, and these reciprocal cerebro-cerebellar connections likely contribute to the variety of cerebellar functions that extend beyond the motor domain (Strick et al. 2009). Recent evidence for reciprocal cerebro-cerebellar interactions has found, for example, that neurons in the CN display preparatory ramping activity related to planning future movement when holding a short-term memory, or anticipating a reward, as is known to occur in the frontal cortex (Gao et al. 2018; Chabrol et al. 2019). Inactivating the cerebellar fastigial nuclei (Gao et al. 2018) or lateral nuclei (Chabrol et al. 2019) disrupts preparatory activity in the frontal cortex, and inactivating the frontal cortex abolishes preparatory activity in the CN. Such findings therefore point to cerebro-cerebellar communication being important for anticipating and planning future actions. An increasing number of studies have studied neural oscillations in cerebro-­ cerebellar circuits. Oscillations are rhythmic patterns of synchronous neural activity that can occur within local circuits and also between distant brain regions. They are thought to be important for input selection, plasticity, and communication between brain regions (Buzsáki and Draguhn 2004; Fries 2015). In the cerebellum oscillations occur at a wide range of frequencies (De Zeeuw et al. 2008), although their functional significance remains far from being clear. However, during whisking in rats, inactivation of the rat cerebellum disrupts coherent neural oscillations between the sensory and motor cortices (Popa et al. 2013). This raises the interesting possibility that the cerebellum may modulate cerebral processing by coordinating communication between cerebral regions. Clearly, however, much remains to be done to gain a full understanding of the functional significance of oscillatory activity within cerebro-cerebellar circuits.

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5 Behavioral Physiology 5.1 Limb Control 5.1.1 Locomotion The cerebellum is involved in the control of locomotion, and accordingly cerebellar damage can result in ataxia (Morton and Bastian 2004). Studies of cerebellar neuronal activity during locomotion, mainly in cats during treadmill or horizontal ladder walking, have found that PCs discharge simple spikes rhythmically in a manner that is time locked to the step cycle. Typically, PCs have one period of increased simple spike discharge per step, but some may have two or three peaks (Armstrong and Edgley 1984b), and the timing of discharge differs between cerebellar cortical regions. In the vermal B zone in decerebrate cats, Udo et al. (1981) reported two profiles of PC simple spike discharge—one population of PCs had a peak of activity in the late swing or early stance phase of the step cycle of the ipsilateral forelimb, and a second population had two peaks: one during late swing, the other during late stance/early swing. The pattern of activity during late swing may relate to the preparation of limb touchdown, consistent with vermal regions of the cerebellum being involved in maintenance of stance and balance (Morton and Bastian 2004). Moreover, while the pattern of complex spike activity in B zone PCs in awake cats occurs without any clear relationship to the step cycle, an increase in probability occurs following a perturbation, as has been shown following an unexpected rung drop during horizontal ladder walking (Andersson and Armstrong 1987). This suggests that climbing fibers can signal unexpected events or “errors” during a predictable movement (see Sect. 5.3.1). In the neighboring C1 zone in the paravermis, the peak of simple spike activity consistently occurs during the swing phase of the ipsilateral forelimb (Armstrong and Edgley 1984b). Activity of neurons in the anterior interpositus, which receive projections from C1 zone PCs, follows the same pattern of activity (Armstrong and Edgley 1984a, b). This suggests that, instead of shaping CN activity through inhibition, PCs may instead dampen excitatory drive to CN neurons in this case (for other ways in which PCs may influence CN activity, see Sect. 3.2). Simple spike activity of PCs in the paravermal C2 and C3 zones occurs slightly later in the step cycle, with peak activity at the transition between the stance and swing phases (Edgley and Lidierth 1988). Differences are also present across the mediolateral width of the C2 zone (Edgley and Lidierth 1988). This suggests that, rather than a systematic shift in the patterns of activity between cortical zones, there is a mediolateral gradient, with more medially located PCs in the paravermis discharging earlier in the step cycle. In keeping with this trend, PCs in the hemispheral D zones tend to have peak activity in the swing phase of the step cycle of the ipsilateral forelimb while walking on a horizontal circular ladder (Marple-Horvat and Criado 1999). The same is also

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the case for dentate nuclear cells. Thus, in agreement with Armstrong and Edgley (1984b), the pattern of modulation of nuclear activity parallels that of the overlying PCs from which the cells receive inhibitory input (Marple-Horvat and Criado 1999). Approximately 45% of lateral cerebellar neurons (cortical and nuclear) were found to be responsive to visual cues, over two-thirds of which also showed rhythmic modulation in relation to the step cycle (Marple-Horvat et al. 1998). Taken together this suggests that D zone activity may be related to visually guided coordination of eye and body movements (Marple-Horvat and Criado 1999), consistent with control of whole body movements discussed above (Sect. 4.1). Studies in rats freely traversing a track confirm that PC activity in lobules V and VI of the vermis is rhythmic during locomotion, and in addition that the patterns of modulation become variable due to other behavioral factors such as speed and acceleration (Sauerbrei Britton et al. 2015). Rhythmic patterns of cerebellar activity are therefore present during locomotion with a forced rhythm or stepping distance, as is the case with treadmill and ladder walking described above, and that which is self-initiated. 5.1.2 Reaching Cerebellar disorders can result in deficits in reaching and grasping movements (Nowak et al. 2013; Zackowski et al. 2002). Cerebellar neurons, particularly those in paravermal regions, encode various components of single and multi-joint limb movements involved in reaching but the patterns of activity are quite mixed. For example, in monkeys performing arm and hand-based targeting tasks, activity of PCs is often modulated in relation to movement velocity, but may also correlate with position or acceleration, and can be tuned to preferred direction(s) (Hewitt et al. 2011; Marple-Horvat and Stein 1987; Fortier et al. 1989). The change in activity usually precedes movements, and an increase in PC discharge is most common with bursts of simple spikes positively correlated with limb muscle activity, although a proportion of PCs also show decreases in activity (Holdefer and Miller 2009). Generally speaking, individual PCs therefore display quite variable patterns of activity during even stereotypical movements such as reach-to-grasp. The reason for this variability is unclear but may in part be due to sampling PCs from different cerebellar zones (as is the case for studies of locomotion). Further investigations are needed in which PCs are studied in relation to cerebellar cortical modules and their microzonal subunits in order to gain a full understanding of cerebellar information processing. Activity of CN neurons can also precede reaching movements and correlate with velocity, position, and acceleration, as well as have a preferred direction (Marple-­ Horvat and Stein 1987). A large proportion of interpositus neurons in the monkey increase their activity preferentially during a reach-to-grasp movement but only when the movement included grasping (van Kan et al. 1994). This suggests that the interpositus may be important in the control of grasping an object but not necessarily in the control of directing the limb to grasp the object. In contrast, a more recent

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study in mice performing a forelimb reaching task found that the activity of interpositus neurons was modulated near the endpoint of a reach and was therefore likely related to limb deceleration to enhance accuracy (Becker and Person 2019). Besides possible species differences, subpopulations of neurons within the cerebellar nuclei (potentially relating to the output of different cerebellar modules) may encode different aspects of complex, multi-joint limb movements, including activation or inactivation of relevant effector muscles. Other cerebellar regions may also encode reaching-relevant information. For example, PCs in the D2 zone of the lateral cat cerebellum encode visual information related to a predictable moving target, and this activity continues even in the temporary absence of the target, consistent with PCs encoding a feedforward prediction (internal model) of the expected movement (Cerminara et al. 2009). The role of the cerebellum as a feedforward controller is further discussed in Sect. 6.

5.2 Eye Movements The posteromedial cerebellum, flocculus, and paraflocculus are involved in the optimization of eye movements, including saccades and smooth pursuit, via vestibular nuclear inputs to oculomotor neurons. As such, saccade dysmetria and nystagmus are often observed in cerebellar patients (Moscovich et al. 2015). Caudal fastigial nucleus neurons in the monkey discharge a burst of action potentials for almost every saccade, and the timing of these bursts suggests this signal is related to the start of contraversive saccades and the end of ipsiversive saccades—perhaps relating to acceleration and deceleration respectively (Fuchs et al. 1993; Robinson and Fuchs 2001). Caudal fastigial activity precedes smooth pursuit onset, and floccular PCs burst after the onset of movement so may be involved in maintaining smooth pursuit (Robinson and Fuchs 2001). In accordance with its control of eye movements, one classical example of cerebellar learning is the vestibulo-ocular reflex (VOR), which is further described in Sect. 5.3.2.

5.3 Associative Learning Early theories of cerebellar learning by Marr (1969) proposed that climbing fibers provide an “error” signal to the cerebellum to induce learning or refinement of movements, and were extended by Albus (1971) to suggest that this involved depression of parallel fiber–PC synapses. Ito and Kano (1982) showed that parallel fiber activation in conjunction with climbing fiber activation was able to induce LTD at this synapse in the cerebellum, using electrical stimulation in decerebrate rabbits, providing early evidence of cerebellar synaptic plasticity and the role of climbing fibers in cerebellar learning. It is now known that the interval between mossy fiber and climbing fiber signals, which induces plasticity, varies across cerebellar regions

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(ranging from ~0 to 150 milliseconds), accounting for the range of feedback delays relevant to the behavioral functions of each region (Suvrathan et al. 2016). Associative learning in the cerebellum can be demonstrated by Pavlovian classical conditioning, where a previously neutral conditioned stimulus (CS) elicits a behavioral response after repeated presentations with an unconditioned stimulus (US). The US is thought to be signaled to the cerebellum via the inferior olive by climbing fibers, while the CS is conveyed by mossy fibers via the pontine nuclei (Steinmetz et al. 1989). These two pathways converge in the cerebellum, in both the cerebellar cortex and CN, and the sites of anatomical convergence are thought to underlie learning of the association between the two inputs. In line with the roles of the cerebellum in eye movements, two of the most widely studied forms of learning involve the eye—eyeblink conditioning and the VOR. 5.3.1 Eyeblink Conditioning Eyeblink conditioning is perhaps the best studied form of cerebellar learning. An air puff to the eye (US) is repeatedly paired with a CS, such as a tone, so that after learning the CS alone induces an eyeblink response. The underlying circuitry has been shown in a range of species to depend upon discrete cerebellar microzones in hemispheric lobule VI with cortico-nuclear projections to anterior interpositus. This cerebellar nuclear region, in turn, has projections to the facial nucleus via the red nucleus (Yeo et al. 1984, 1985a, b; Ten Brinke et al. 2019). Studies of eyeblink conditioning have provided evidence of a clear link between changes in patterns of neural activity and learnt behavior. Presentation of a novel tone or light stimulus (equivalent to a CS) alone may evoke climbing fiber activity; however, this response reduces with repeated presentations as saliency decreases (Ohmae and Medina 2015). During CS–US pairings in early acquisition of eyeblink conditioning, however, the US (air puff) evokes a climbing fiber response that drives learning of the conditioned eyelid response in response to the CS; in later stages of learning, the eyelid closure is initiated after the CS in anticipation of the air puff (Sears and Steinmetz 1991; Medina et al. 2002). Once the conditioned response is acquired, climbing fibers fire in response to the predictive stimulus (CS) (Ohmae and Medina 2015). Conditioned eyelid closure is associated with simple spike suppression in PCs, which results in disinhibition of CN neurons (Johansson et  al. 2014; Hesslow and Ivarsson 1994; Rasmussen et al. 2008). Increased activity of CN neurons during expression of the conditioned response subsequently inhibits climbing fiber activity driven by the US in the inferior olive, due to increased inhibition via nucleo-olivary projections (Medina et al. 2002; Rasmussen et al. 2008). During extinction learning, the CS is repeatedly presented without the US so it is learned that the expression of the defensive response is no longer required (Jirenhed et al. 2007). Climbing fiber inhibition resulting from the conditioned response (increased CN inhibition of the inferior olive) in the absence of the US is thought to be an important teaching signal for extinction (Ohmae and Medina 2015). Long-term synaptic plasticity at parallel fiber to PC synapses is thought to underlie eyeblink conditioning, resulting in the suppression of PC firing as described

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above. Paired stimulation of parallel fibers and climbing fibers, corresponding to the CS and US respectively, has been shown to induce LTD at parallel fiber–PC synapses, and interfering with metabotropic glutamate receptors and protein kinase C pathways involved in parallel fiber–PC LTD has been shown to inhibit synaptic plasticity and learning of the conditioned eyeblink response (Aiba et  al. 1994; Koekkoek et  al. 2003). However, Schonewille et  al. (2011) found no significant impairment in eyeblink conditioning when a later step in the LTD pathway, AMPA receptor internalization, was blocked in three types of mutant mice. The authors argued that impairments produced by manipulating earlier steps in the LTD pathway may be related to cellular processes other than LTD. A subsequent study by Yamaguchi et al. (2016) using two of these mutant mouse models found that while conventional protocols did not induce LTD in these mice, LTD could be induced in vitro using intensified stimulation protocols, for example pairs of parallel fiber stimulations combined with PC depolarization at 1 Hz for 3 minutes (as opposed to a single parallel fiber and climbing fiber stimulus at 1  Hz for 5 minutes). Compensatory mechanisms may therefore be at play in mice with disrupted LTD mechanisms, although further experiments are required to investigate how any of these protocols relate to physiological LTD in vivo. In summary, despite the attractiveness of LTD at the parallel fiber–PC synapse being the cellular mechanism underpinning cerebellar contributions to motor learning, evidence remains lacking to show conclusively that this is the case. 5.3.2 Vestibulo-Ocular Reflex The VOR is a gaze stabilizing reflex that produces eye movements opposing the direction of head movements. In an experimental setting, VOR can be manipulated by moving the head and visual inputs in the same or opposite directions at varying speeds and amplitudes, with eye movements adapting to each novel configuration to re-stabilize vision. The flocculus and ventral paraflocculus of the cerebellum receive visual error signals, which converge with vestibular and eye movement information (Frens et al. 2001; Noda 1986). Simple spike modulation of PCs in these regions correlates with head and eye position, and VOR adaptation drives changes in modulation of PC activity (De Zeeuw et al. 1995; De Zeeuw and Ten Brinke 2015). These PCs inhibit the vestibular nuclei, which project to oculomotor nuclei to control muscles of the eyes. Genetically modified mice lacking protein phosphatase 2B, which is involved in parallel fiber–PC LTP and modifying the intrinsic excitability of PCs, show VOR adaptation deficits (Schonewille et  al. 2010). Mice deficient in parallel fiber–PC LTD are still able to show VOR adaptations (Schonewille et al. 2011), suggesting that under such experimental conditions LTP may be the most important form of plasticity for this type of learning. However, this may not be true for all forms of VOR adaptation (for example, opposite gain modulation), with multiple forms of plasticity likely to contribute under physiological conditions when all signaling pathways are intact (Boyden et al. 2004; Kimpo et al. 2014).

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5.3.3 Higher-Order Learning Associative learning principles may also apply to higher-order forms of learning in which the cerebellum is involved—in particular, reward-based learning, as the cerebellum has been shown to encode several aspects of reward. For example, granule cells in mice encode expectation of reward (Wagner et al. 2017) and climbing fibers signal reward prediction in the lateral cerebellum (lobule simplex, Crus I and II) during learning (Heffley and Hull 2019). Climbing fibers can also signal reward delivery and omission, which map onto different cerebellar cortical microzones— reward delivery causes activation in a subset of microzones within lobules V and VI and suppression in others, whereas reward omission activates both sets of microzones (Kostadinov et al. 2020). The reward omission signal conveyed by the climbing fiber system may be an “error” signal that occurs when the outcome is unexpected, in accordance with error-based theories of climbing fiber function (e.g., Zang and De Schutter 2019) and classical theories of cerebellar-dependent motor learning (Marr 1969). Supporting this idea, climbing fiber responses to predictable rewards are suppressed during learning (Kostadinov et al. 2020), and the phenomenon may be generalized to the cerebellar mossy fiber–granule cell–parallel fiber system because reward-­ related error signals in PC simple spike responses diminish as monkeys learn a reward-association task (Sendhilnathan et al. 2020). During trial-and-error-based visuomotor association learning, PC simple spikes encode the outcome of the monkey’s most recent decision throughout the subsequent trial, updating with each trial and decreasing with improved performance (Sendhilnathan et al. 2020). Cognitive learning in the cerebellum could therefore be driven by similar mechanisms as error-based motor learning, with learning in both motor and cognitive domains involving testing predictions against actual outcomes. However, this may not be true for all types of behavior given that climbing fibers do not always signal error, as explored in the next section. 5.3.4 Climbing Fibers and Learning Providing an error signal may not be the universal function of climbing fiber inputs to the cerebellum, since varying patterns of PC complex spike activity during learning have been observed. For example, complex spikes in the posterior vermis of the monkey occur randomly before saccadic adaptation, yet a distinct response profile emerges (with an increase or decrease in probability of occurrence depending on direction of adaptation) during the adaptation process that may act to stabilize the learned behavior (Catz et al. 2005); the opposite might be expected if the complex spikes signaled error, as error signals would decrease with learning. In reward-­ driven behaviors, which require a cognitive component, complex spikes have been shown to signal reward prediction and thus may guide cerebellar learning in a feedforward, predictive manner (Heffley and Hull 2019).

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Complex spike responses may adapt to respond preferentially to salient sensory cues over less behaviorally relevant cues (Bina et al. 2021). For example, complex spikes can adapt to occur in response to a tactile reward-related cue rather than a neutral auditory cue, which in turn promotes potentiation of simple spike responses to the salient cue (Bina et al. 2021). Climbing fibers, and resulting complex spikes, therefore can drive learning, but this may occur in different ways depending upon the type of behavior, the region of the cerebellum, and the larger brain networks involved. Simple spikes themselves may also signal error in motor behaviors as well as movement kinematics (Popa et al. 2012), providing an alternative route through which error-based learning may occur. As outlined above (Sect. 3.1), climbing fibers have long been thought to carry an “all or nothing” signal (Eccles et al. 1966), but more recent work suggests that information may also be conveyed in the waveform of individual complex spikes (Burroughs et al. 2017; Zang and De Schutter 2019). For example, the amplitude of PC calcium signals triggered by climbing fiber synapses in mice is enhanced when there is a sensory event, which may drive plasticity (Najafi et al. 2014). Also, during learning of smooth eye pursuit movements in the monkey, longer climbing fiber bursts lead to longer-duration complex spikes (and by inference a larger number of spikelets) that promote plasticity and enhance learning (Yang and Lisberger 2014). Similarly, the number of spikelets in a complex spike (Fig. 3) has been shown to increase following acquisition of delay eyeblink conditioning in mice (Titley et al. 2020). Thus, the relationship between complex spikes and learning extends beyond an all or none action potential type event and is likely to contribute to the diversity of climbing fiber function in learning. Clearly this is a subject that merits further study, particularly in light of the systematic differences in complex spike waveform related to synchrony (Sect. 3.1) and also to zebrin topography (Sect. 3.4).

6 The Cerebellum as a Feedforward Controller In terms of motor control, the cerebellum is thought to generate internal models (feedforward predictions) about the sensory outcomes of intended movements and update these using movement-related sensory feedback (Fig. 6). The sensory prediction error generated can then guide movements online, account for sensory reafference occurring from self-generated movement, and guide motor learning (Popa and Ebner 2019). The fact that loops such as cortico-nucleo-olivary circuits and reciprocal connectivity with regions of the cerebral cortex are at the heart of cerebellar connectivity makes it well suited as a feedforward controller. Recently described inhibitory projections from the interpositus nucleus to sensory-related areas in the brainstem (Sect. 3.2) add extra feedback loops that could in theory modulate predictions of actions and sensory reafference (Judd et al. 2021). There is an increasing body of evidence to support the cerebellar feedforward prediction model, whereby a forecast of the consequences of an action is made before the action is completed (e.g., Miall and Wolpert 1996; Miall et  al. 1998;

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Fig. 6  A simplified schematic of circuits showing how the cerebellum acts as a prediction machine. Motor and non-motor actions such as goal-directed movements and problem solving occur following commands from cerebral areas (e.g., motor and prefrontal cortices, respectively) via connections to effector systems, such as the spinal cord for motor commands and association areas of the cerebral cortex for cognitive commands (behavior). An efference copy of these commands is sent to the cerebellum, via the pontine nuclei, and to the inferior olive (IO). Following behavior, feedback is delivered to the cerebellum via the IO and also to cerebral areas, and the IO compares cerebellar predictions to feedback to generate an error signal. The cerebellum compares the intended action (efference copy) and IO error signal to update its prediction. The prediction generated by the cerebellum allows online corrections of behavior through cerebellar connections to effector systems and updates the command in cerebral areas via the thalamus

Kitazawa et al. 1998; Cerminara et al. 2009; Ishikawa et al. 2016). In a recent example, recordings from a monkey during step tracking movements of the wrist found that activity in the dentate nucleus could predict the firing rate of mossy fibers, suggesting that the cerebellum is able to predict upcoming sensory inputs (Tanaka et al. 2019). There is also evidence from human subjects with cerebellar degeneration, who show impaired adaptive abilities during reaching and speech production compared to controls, suggesting problems with feedforward processing but not with compensatory responses, indicating that feedback systems are still intact (Parrell et al. 2021). An extension of the internal model theory is to consider the cerebellum more generally as a “prediction machine” (Ramnani et al. 2000; Hull 2020). This expands cerebellar involvement in relatively simple circuits underpinning specific forms of motor learning, for example eyeblink conditioning, to more complex neocortical prediction paradigms involving interactions between multiple brain regions (Fig. 6). The reciprocal connections of the cerebellum with a multitude of brain structures provide the anatomical substrate to be involved in sensory, motor, and cognitive processes (Welniarz et al. 2021). It is therefore perhaps unsurprising that PCs are able to encode predictive and feedback signals of both movement and task performance, the latter associated with cognitive involvement (Popa and Ebner 2019). Such findings suggest that cognitive processing contributes to cerebellar-mediated learning by providing information about whether or not an action was successful

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(Popa and Ebner 2019), just as reward signals can reinforce accuracy of goal-­ directed movements as described in Sect. 5.3.3.

7 Summary The cerebellum is traditionally thought of as a brain structure with a highly regular cytoarchitecture, concerned primarily with motor control. However, we now know that there are additional complexities to cerebellar circuits, including recurrent loops both within the cerebellum and between the cerebellum and other brain regions, as well as systematic anatomical and physiological differences between cerebellar regions that likely relate to regional specialization of function. Nevertheless, a feature that may be common to all cerebellar circuits is the computation of differences between expected and actual outcomes of behavior, in all its different forms—enabling the cerebellum to regulate a wide variety of motor and non-motor functions via general principles applied to different brain networks. A key challenge for future research is to understand how the physiology of the cerebellum enables it to function as a universal prediction machine.

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Cerebellar Biochemistry/Pharmacology Takahiro Seki

Abstract  Cerebellar Purkinje cells (PCs) are localized in the cerebellar cortex and are characterized by highly developed dendrites, which receive different inputs from the parallel fibers of cerebellar granule cells, climbing fibers of inferior olive neurons in the medulla, and other cerebellar interneurons. PCs are the sole output neurons of the cerebellar cortex and are crucial for cerebellar functions. PC degeneration and morphological changes in PC dendrites are frequently observed in postmortem patients and mouse models of various cerebellar ataxias. Therefore, the factors that regulate the survival and morphology of PCs may be associated with the pathogenesis and progression of cerebellar ataxia. In this chapter, I summarize the interactions between PCs and other cerebellar cells that affect the survival and morphology of cerebellar PCs. Furthermore, numerous studies have revealed that intracellular protein degradation systems contribute to the maintenance of protein homeostasis and are essential for neuronal survival and function retention. Therefore, I also outline the role of protein degradation systems in the regulation of the survival and function of cerebellar neurons. Lastly, I briefly describe the endogenous modulators that affect the survival and morphology of cerebellar PCs. Keywords  Purkinje cells · Parallel fibers · Climbing fibers · Ubiquitin-proteasome system · Autophagy-lysosome pathways

T. Seki (*) Department of Chemico-Pharmacological Science, Graduate School of Pharmaceutical Sciences, Kumamoto University, Kumamoto, Japan Department of Pharmacology, Faculty of Pharmaceutical Sciences, Himeji Dokkyo University, Himeji, Japan e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_3

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1 Introduction The cerebellum was traditionally considered to be involved in motor control and motor learning. However, increasing evidence has indicated that it also participates in cognitive processing and emotional control (Schmahmann and Caplan 2006), with different cerebellar areas involved in regulating motor and cognitive functions. The anterior vermis of the cerebellum mainly regulates motor function, while the posterior vermis and lateral hemispheres of the cerebellum control cognitive and emotional functions (Stoodley et al. 2016). Despite the regional differences in cerebellar function, the cerebellum comprises a uniform structure of layers. The cerebellar cortex consists of three layers: the molecular layer, Purkinje cell (PC) layer, and granule cell layer (Fig. 1). Among the cerebellar cortex neurons, PCs play a central role in regulating cerebellar function (Cerminara et al. 2015; Kalinichenko and Pushchin 2018; van der Heijden and Sillitoe 2021). Somata of PCs are lined up in the PC layer, while highly developed dendrites of PCs are projected onto the molecular layer. Numerous somata of granule cells are found in the granule cell layer. Granule cells receive inputs from mossy fibers originating from neurons in various regions, including the pons, medulla, midbrain, and spinal cord, and

Fig. 1  Schematic illustration of layer structure, cell types, and neural connections in the cerebellum

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innervate the dendrites of PCs as parallel fibers. Dendrites of PCs also receive inputs from climbing fibers originating from the inferior olive neurons in the medulla (Kalinichenko and Pushchin 2018). However, each PC is innervated by a single climbing fiber at the proximal dendrites, whereas each PC receives inputs from parallel fibers of many granule cells at the distal dendrites. The parallel and climbing fibers form excitatory glutamatergic synapses with PC dendrites. The activities of PCs are also regulated by γ-aminobutyric acid (GABA)ergic interneurons (stellate and basket cells) localized in the molecular cell layer (Kalinichenko and Pushchin 2018). There are several inhibitory interneurons localized in the granule layer, including Golgi and Lugaro cells. Golgi cells regulate excitatory inputs from mossy fibers to granule cells. Lugaro cells regulate other inhibitory interneurons. Although the cerebellar cortex receives inputs from mossy and climbing fibers originating from other brain regions, PCs are the sole output neurons of the cerebellar cortex that go on to innervate the neurons in deep cerebellar nuclei (DCN) (Fig. 1) (D’Angelo et al. 2011). Therefore, PCs are crucial for cerebellar function. Functional or morphological aberrations or degeneration of PCs is frequently observed in patients with ataxia and ataxic animal models (Klockgether et al. 2019; Koeppen 2018). Most spontaneous ataxic mutant animals exhibit degeneration or dysfunction of the PCs (Cendelin 2014). Genetic analyses of these spontaneous ataxic mutant animals have revealed factors that regulate the dendritic morphology and survival of PCs. The regulation of dendritic development has also been investigated using organotypic or dissociated cerebellar cultures. In this chapter, I focus on the interactions between PCs and other cerebellar cells, protein degradation systems, and endogenous modulators that affect the morphology, survival, and function of PCs. Natural mutant ataxic model animals, treatment-induced ataxic model animals, and abbreviations that I describe in this chapter are listed in Tables 1, 2, and 3, respectively. Magenta and cyan triangles indicate excitatory (glutamatergic) and inhibitory (GABAergic) inputs. Black and blue lines indicate axons and dendrites of cerebellar neurons, respectively.

2 Interactions Between Purkinje Cells and Other Cerebellar Cells 2.1 Parallel and Climbing Fibers (Granule Cells and Inferior Olive Neurons) Glutamatergic input from parallel and climbing fibers is related to the synaptic plasticity (formation of long-term potentiation [LTP] and long-term depression [LTD]) of PCs as well as motor coordination and motor learning (Ito 2001; Vogt and Canepari 2010). Additionally, input variations from these fibers affect the dendritic morphology and survival of PCs. This aspect has been researched in various animal models, such

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86 Table 1  Natural mutant ataxic animals described in this chapter Name Weaver Reeler Shaker

Pingu

Long Evans Shaker Tambaleante Purkinje cell degeneration (pcd) Staggerer

Lucher Hotfoot

Wistar Kyoto

Species Gene Mouse Missense mutation of Kcnj6 gene encoding GIRK2 Mouse Deletion mutation of Reln gene encoding reelin Rat Missense mutation of Atp2b3 gene encoding plasma membrane calcium pump isoform 3 (PMCA3) Mouse Missense mutation of Kana2 gene encoding Kv1.2 Rat Deletion mutation of Mbp gene Mouse Missense mutation of Herc1 gene Mouse Deletion mutation of Nna1 gene

Phenotype Reference Loss of granule cells Sotelo (1975) Loss of granule cells Heckroth et al. (1989) Loss of Purkinje Figueroa et al. cells (2016)

Loss of function of Kv1.2

Xie et al. (2010)

Demyelination of Purkinje cells Loss of Purkinje cells Loss of Purkinje cells

Barron et al. (2018) Wassef et al. (1987) Fernandez-­ Gonzalez et al. (2002) Hamilton et al. (1996)

Mouse Deletion mutation of Rora gene encoding RORα

Impaired development of Purkinje cells Mouse Missense mutation of Grid2 Loss of Purkinje gene encoding GluRδ2 cells Mouse Deletion mutation of Grid2 Impairment of gene encoding GluRδ2 synaptic formation in PCs Cerebellar shrinkage Rat Missense mutation of (Reduced expression Abcg5 gene encoding of CBS in the ATP-binding cassette cerebellum) transporter protein G5 (ABCG5)

Zuo et al. (1997) Lalouette et al. (1998) Nagasawa et al. (2015)

Table 2  Treatment-induced ataxic animals described in this chapter Name X-Irradiation

Species Mouse

3-Acetylpyridine Rat Ibogaine

Rat

Ethanol

Rat, mouse

Phenotype Loss of granule cells Loss of inferior olive neurons and climbing fibers Glutamate excitotoxicity of Purkinje cells from climbing fibers Transient cerebellar ataxia

Reference Sotelo and Dusart (2009) Heckroth et al. (1989) Xu et al. (2000) Saeed Dar (2015)

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Table 3  List of abbreviations Abbreviation AIS ALP AMPA 3-AP Atg7 BDNF CBS cGMP CHIP CMA CSE DAO DCN EA EAAT ERK GABA GIRK2 GLAST GluRδ2 GLT1 HERC1 Hsc70 LAMP2A LTD LTP MA mA MBP 3MP MS 3MST MVB MNDA nNOS NO NPC PC pcd PI31 PKC PKG

Full spell Axonal initial segment Autophagy-lysosome pathway α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid 3-Acetylpyridine Autophagy-related gene 7 Brain-derived neurotrophic factor Cystathionine β-synthase Cyclic guanosine monophosphate C-terminus of Hsc70-interacting protein Chaperone-mediated autophagy Cystathionine γ-lyase D-Amino acid oxidase Deep cerebellar nuclei Episodic ataxia Excitatory amino acid transporter Extracellular signal-regulated kinase γ-Aminobutyric acid G-protein-regulated inward-rectifier potassium channel 2 Glutamate/aspartate transporter Glutamate receptor δ2 Glial glutamate transporter 1 HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 Heat shock cognate protein 70 kDa Lysosome-associated membrane protein 2A Long-term depression Long-term potentiation Macroautophagy Microautophagy Myelin basic protein 3-Mercaptopyruvate Multiple sclerosis 3-Mercaptopyruvate sulfur transferase Multivesicular body N-methyl-D-aspartate Neural nitric oxide synthase Nitric oxide Niemann-Pick disease type C Purkinje cell Purkinje cell degeneration Proteasomal inhibitor of 31 kDa Protein kinase C Protein kinase G (continued)

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Table 3 (continued) Abbreviation RORα RyR1 SCA tbl TH TRE TRH UPS

Full spell Receptor-related orphan receptor α Type 1 ryanodine receptor Spinocerebellar ataxia Tambaleante Thyroid hormone TH response element Thyrotropin-releasing hormone Ubiquitin-proteasome system

as weaver mice, which are natural mutant mice with a missense mutation in G-proteinregulated inward-rectifier potassium channel 2 (GIRK2) (Hess 1996). GIRK2 is mainly expressed in granule cells in the cerebellum. The weaver mutation of GIRK2 hampers inwardly rectifying K+ current (Surmeier et al. 1996), leading to the loss of granule cells is observed in early cerebellar development (Sotelo 1975). Despite the loss of parallel fibers derived from granule cells, PC dendrites are developed in weaver mice. However, the length of the dendrites is smaller, and the orientation of the dendrites is disturbed (Sotelo 1975). Similar findings have been reported in other mutant mice, such as reeler mice, in which granule cells also degenerate at the developmental stage (Heckroth et al. 1989), and in agranular rats, which are postnatally X-irradiated to deplete granule cells (Sotelo and Dusart 2009). The weaver and reeler mice also show a reduction in PCs in the cerebellum (Heckroth et  al. 1989; Sotelo 1975). Additionally, PC degeneration is also observed in shaker rats which have a missense mutation of Atp2b3 gene encoding plasma membrane calcium pump isoform 3 (PMCA3) (Figueroa et al. 2016). A missense mutation of PMCA3 is also identified in patients with X-linked congenital cerebellar ataxia (Zanni et  al. 2012). PMCA3 is expressed in the presynaptic terminals of parallel fibers that project to PCs (Burette and Weinberg 2007). PMCA3 has a role in maintenance of intracellular Ca2+ homeostasis to reduce cytosolic Ca2+ after the transient Ca2+ increase (Brini and Carafoli 2009). A missense mutation of PMCA3 found in patients delays the clearance of cytosolic Ca2+ (Zanni et al. 2012), which would result in dysregulation of neurotransmitter release from parallel fibers. In vitro studies using purified PCs from embryonic mouse cerebella have shown that granule cells are essential for the complete development of PC dendrites and survival of PCs in dissociated cultures (Carlos et al. 1994). Although glial cells and neurons from other regions partially help in the survival of purified PCs, these cells do not have adequately developed dendrites. These findings indicate that inputs from the parallel fibers of granule cells are necessary for the normal development of PC dendrites and PC survival. The innervation of PCs by the climbing fibers of inferior olive neurons has been investigated using 3-acetylpyridine (3-AP), a toxin of inferior olive neurons. 3-AP is frequently used to develop drug-induced ataxic rat models (Llinás et al. 1975). 3-AP functions as a metabolic antagonist and decreases nicotinamide, leading to the inhibition of nicotinamide nucleotide dinucleotide (NAD+)-dependent reactions. The administration of 3-AP followed by nicotinamide triggers the selective

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degeneration of inferior olive neurons (Llinás et al. 1975). The anti-ataxic effect of taltirelin, a thyrotropin-releasing hormone that is clinically used as a therapeutic agent for spinocerebellar ataxia (SCA), has been evaluated in 3-AP-induced ataxic rats (Kinoshita et al. 1995). Several reports have indicated that 3-AP triggers the neurodegeneration of PCs (Chong et al. 2020; Mahmoudi et al. 2019). Additionally, 3-AP accelerates PC degeneration in shaker mutant rats (Tolbert and Clark 2000). These findings indicate that climbing fibers from inferior olive neurons contribute to PC survival. However, other studies have demonstrated that the loss of climbing fibers induced by 3-AP does not affect the survival of PCs but affects their electrophysiological properties, leading to an ataxic phenotype (Kaffashian et  al. 2011; Rossi et  al. 1991). Although the role of climbing fibers in the survival of PCs is controversial, the importance of these inputs for motor coordination is established. Ibogaine is an indole alkaloid extracted from Tabernanthe iboga that triggers the degeneration of PCs in the rat cerebellum (Xu et al. 2000). However, this neurotoxicity is not caused by a direct effect of ibogaine on PCs but is mediated by the climbing fibers, as demonstrated by the elimination of this toxicity by 3-AP-induced ablation of the climbing fibers (O’Hearn and Molliver 1997). Since at the synapses between PCs and climbing fibers are glutamatergic, glutamate excitotoxicity is involved in ibogaine-triggered degeneration of PCs. Glutamate-induced neurodegeneration of PCs is called dark cell degeneration and is mediated by the activation of α-amino-3hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors and not N-methyl-Daspartate (NMDA) receptors (Garthwaite and Garthwaite 1991; Strahlendorf et  al. 2003). However, ibogaine-triggered degeneration of PCs is not inhibited but enhanced by treatment with an AMPA receptor antagonist (O’Hearn and Molliver 2004). Although PCs are classically considered not to express NMDA receptors, functional NMDA receptors are reported to be postsynaptically expressed at synapses between PCs and climbing fibers (Piochon et al. 2010). These findings suggest that glutamate toxicity through the activation of NMDA receptors at PC-climbing fiber synapses could cause ibogaine-triggered neurodegeneration of PCs. Thus, excessive excitation of climbing fibers triggers the neurodegeneration of PCs. Figure 2 summarizes molecular mechanisms in PC degeneration and PC dendritic shrinkage by the dysfunctions of parallel and climbing fibers. Fig. 2 Molecular mechanisms in PC degeneration and PC dendritic shrinkage by the dysfunctions of parallel and climbing fibers

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2.2 Basket Cells GABAergic basket cells innervate the PC somata and axonal initial segments (AIS) of PCs (Fig. 1). Nerve terminals from basket cells surround the AIS and form characteristic structures called pinceau (Somogyi and Hámori 1976). The voltage-gated potassium channel α-subunits (Kv1.1 and Kv1.2) are concentrated in these pinceau structures (Wang et al. 1994). Mutations in these subunits induce ataxic phenotypes. Missense mutations in Kv1.1 are associated with episodic ataxia 1 (EA1), which is characterized by stress-induced and recurrent attacks of ataxia (Browne et al. 1994). EA1 model mice, which carry the heterozygous missense mutation (V408A) in Kv1.1, show stress-induced motor impairment (Herson et al. 2003). This ataxic phenotype is ameliorated by treatment with acetazolamide, a clinical therapeutic agent used for episodic ataxia (Zasorin et al. 1983). Electrophysiological studies showed increased GABAergic input to PCs in EA1 model mice (Herson et al. 2003), a phenotype similar to that of Kv1.1-deficient mice (Zhang et al. 1999). Pingu mice that are generated by treatment with a chemical mutagen (N-ethyl-N-nitrosourea) carry a missense mutation in Kv1.2 and present with chronic motor incoordination (Xie et al. 2010). The motor incoordination in Pingu mice is ameliorated by treatment with acetazolamide, similar to that observed in EA1 model mice (Herson et  al. 2003). Although electrophysiological properties are not affected in Pingu mice, the missense mutation in Kv1.2 makes this protein unstable (Xie et al. 2010). These findings indicate that loss of function of Kv1.1 and Kv1.2 caused by missense mutations in these proteins leads to the development of the ataxic phenotype. Since α-dendrotoxin, an inhibitor of Kv1.1 and Kv1.2, enhances inhibitory synaptic inputs from basket cells to PCs (Southan and Robertson 1998), loss of these channels would trigger hyperexcitation of basket cells and excessive inhibition of PCs. Acetazolamide-mediated intracellular alkalinization may reduce excitability of the basket cells (Herson et al. 2003). These findings suggest that inhibitory input from basket cells to PCs is crucial for motor coordination.

2.3 Glial Cells Bergmann glia are radial astrocytes that are associated with PCs (Bellamy 2006). Their somata are closely localized to the PC somata in the PC layer and project processes to the molecular layer (Fig.  1). The radial structure of Bergmann glia contributes to layer formation during the developmental stage (Xu et al. 2013). In the adult cerebellum, Bergmann glia express glial glutamate transporters (including glutamate/aspartate transporter [GLAST]/excitatory amino acid transporter 1 [EAAT1] and glial glutamate transporter 1 [GLT1]/EAAT2) at the radial processes and regulate glutamatergic neurotransmission at the climbing fiber-PC and parallel fiber-PC synapses (Takayasu et  al. 2009). GLAST is specifically expressed in Bergman glia in the cerebellum. Therefore, Bergmann glia regulate the activity and

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synaptic plasticity of PCs through the modulation of synaptic glutamate uptake. Early activation of Bergmann glia, which is characterized by the elevation of glial fibrillary acidic protein, is frequently observed in animal models of cerebellar ataxia, including various types of SCAs (Cvetanovic et al. 2015; Seki et al. 2018a). In particular, a reduction in GLAST is observed in SCA mouse models (Cvetanovic 2015; Noma et al. 2012). The reduction in GLAST may hamper synaptic glutamate uptake, increase glutamate concentration at the synaptic cleft, and trigger glutamate excitotoxicity in PCs, leading to PC degeneration in SCA. Moreover, GLAST-­ deficient mice show motor impairment and increased susceptibility to cerebellar injury (Watase et al. 1998), while the expression of SCA7-causing mutant protein in Bergmann glia leads to non-cell-autonomous degeneration of PCs and the ataxic phenotype through reduction in GLAST (Custer et al. 2006). Furthermore, reduction in Bergmann glia themselves is observed in the cerebella of patients with SCA1 and SCA1 mouse models (Shiwaku et al. 2013). Moreover, a model of astrogliosis that was generated by chronic optogenetic activation of Bergmann glia showed downregulation of GLAST and degeneration of PCs (Shuvaev et  al. 2021). These results suggest that Bergmann glia activation and reduction of Bergmann glia contribute to the pathogenesis and progression of cerebellar ataxia through the dysregulation of glutamate uptake and induction of non-­ cell autonomous PC degeneration. Early inflammatory activation of microglia is commonly observed in animal models of various neurodegenerative diseases (Wolf et al. 2017). In line with these findings, microglial activation may be involved in neurodegeneration. Similarly, microglial activation is observed in the early stages of cerebellar ataxia in ataxic model animals (Ferro et al. 2019). Inhibition of microglial activation via the genetic ablation of myeloid differentiation factor 88, which is involved in the inflammatory activation of microglia (Esen and Kielian 2006), ameliorates PC degeneration and motor dysfunction in SCA6 model mice (Aikawa et  al. 2015). Additionally, lipopolysaccharide-­triggered microglial activation in the cerebellum leads to degeneration of PCs and motor impairment (Hong et al. 2020). However, this treatment also activates astrocytes, including the Bergmann glia. These findings indicate that the inflammatory activation of microglia directly or indirectly triggers cerebellar neurodegeneration in SCA. Cerebellar ataxia commonly occurs in multiple sclerosis (MS), which is caused by the progressive demyelination of neurons in the central nervous system (Wilkins 2017). Loss of PCs and abnormal morphology of PC axons are observed in the cerebella of patients with MS (Redondo et al. 2015). Furthermore, a decrease in the myelination of PC axons results in reduced GABAergic inputs to DCN neurons and the hyperactivation of DCN neurons in Long Evans Shaker rats (Barron et al. 2018), in which myelin basic protein (MBP) is genetically deleted (Delaney et al. 1995). MBP is expressed in oligodendrocytes and is a major constituent of the myelin sheath in the central nervous system (Rumsby and Walker 1980). These results suggest that oligodendrocytes regulate the survival and synaptic transmission of PCs via the myelination of PC axons.

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Fig. 3 Molecular mechanisms in PC degeneration by the aberrant regulation of glial cells

Furthermore, two different groups independently revealed that transcriptional changes in oligodendrocyte-related genes are present in SCA3 model mice (Haas et al. 2021; Ramani et al. 2017). Hass et al. also demonstrated that MBP and oligodendrocyte transcription factor 2, which are expressed in oligodendrocytes, are reduced in the cerebella of patients with SCA3 (Haas et al. 2021). These findings strongly suggest that functional alterations in oligodendrocytes contribute to the pathogenesis of SCA. Figure 3 summarizes molecular mechanisms in PC degeneration by the aberrant regulation of glial cells.

3 Importance of Protein Degradation Systems in Cerebellar Purkinje Cells 3.1 Classification of Protein Degradation Systems Protein degradation systems contribute to maintaining intracellular protein homeostasis in cells. Intracellular protein degradation systems are mainly divided into the ubiquitin-proteasome system (UPS) and autophagy-lysosome pathway (ALP) (Fig. 4) (Dikic 2017; Wang and Le 2019). In the UPS, substrate proteins are selectively polyubiquitinated by various E3 ubiquitin ligases. The polyubiquitinated proteins are then delivered to the proteasome, a large protein complex containing multiple proteases, where ubiquitinated substrates are degraded (Fig.  4a) (Hegde and Upadhya 2011). The ALP consists of three pathways that deliver substrates to lysosomes: macroautophagy (MA), microautophagy (mA), and chaperone-­mediated autophagy (CMA) (Fig. 4b) (Haspel and Choi 2011), among which MA has been the most widely studied (Mizushima and Levine 2020). In MA, substrate proteins are surrounded by isolation membranes, incorporated into autophagosomes, and delivered to lysosomes via the fusion of autophagosomes with lysosomes. In comparison to MA, the physiological roles of mA and CMA are not well understood (Tekirdag and Cuervo 2018). The molecular mechanisms of mA and CMA in mammalian cells has remained unclear until recently. The heat shock cognate protein

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Fig. 4  Schematic illustrations of ubiquitin-proteasome system (a) and autophagy-lysosome pathway (b). Ub: ubiquitin, E1: ubiquitin activation enzyme, E2: ubiquitin conjugating enzyme, E3: E3 ubiquitin ligase

70 kDa (Hsc70), a molecular chaperone, commonly recognizes substrates, which carry specific pentapeptide sequences called KFERQ motifs (Kirchner et al. 2019), for mA and CMA.  In mA, substrates are delivered into late endosomes via the invagination of endosomal membranes, resulting in the formation of multivesicular bodies (MVBs). Next, MVBs deliver the intravesicular substrates for lysosomal degradation by fusing with lysosomes. In CMA, substrates are directly transferred into lysosomes through the lysosomal translocon formed by the oligomerization of lysosome-associated membrane protein 2A (LAMP2A) on the lysosomal membrane. Various studies have demonstrated that decline in intracellular protein degradation is related to the pathogenesis of multiple diseases via the disturbance of protein homeostasis (Hanna et  al. 2019; Mizushima and Levine 2020). In particular,

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non-­dividing neuronal cells are strongly dependent on protein degradation systems for protein homeostasis maintenance (Malgaroli et al. 2006). Impairment of protein degradation systems has been frequently observed in patients and animal models of neurodegenerative diseases (Ciechanover and Kwon 2015; Douglas and Dillin 2010). In support with these findings, neuron-specific knockout of proteasome- or MA-related proteins have been found to induce phenotypes similar to neurodegenerative diseases, including accumulation of misfolded proteins and neurodegeneration (Hara et  al. 2006; Komatsu et  al. 2006; Tashiro et  al. 2012). Moreover, age-related decline in protein degradation may be related to the age-related pathogenesis of various neurodegenerative diseases (Douglas and Dillin 2010).

3.2 Ubiquitin-Proteasome System in Cerebellar Purkinje Cells Tambaleante (tbl) mutant mice show progressive degeneration of cerebellar PCs and a severe ataxic phenotype inherited in an autosomal recessive manner (Wassef et  al. 1987). This phenotype is caused by a missense mutation in the HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), a ubiquitin ligase protein (Mashimo et  al. 2009). Transgenic rescue of wild-type HERC1 (Mashimo et al. 2009) indicates that the loss of function of HERC1 causes this cerebellar phenotype. Indeed, a nonsense mutation of HERC1 gene is identified in a patient with megalencephaly accompanied with cerebellar atrophy (Nguyen et al. 2016). HERC1 regulates extracellular signal-regulated kinase (ERK) signaling via the degradation of C-RAF that phosphorylates and activates ERK (Schneider et al. 2018). Yang et al. reported that the upregulation of C-RAF impairs synapse formation in cultured cerebellar granule cells (Yang et  al. 2013). Therefore, an increase in C-RAF that is caused by the loss of function of HERC1 might reduce synaptic inputs from parallel fiber to PCs, leading to the degeneration of PCs. Additionally, axonal degeneration and loss of PCs are triggered by the PC-specific knockout of proteasomal inhibitor of 31 kDa (PI31), a proteasome-binding protein (Minis et al. 2019). Although PI31 was first identified as an inhibitor of proteasomes in vitro, it has been reported to serve as an adapter protein for the axonal transport of proteasomes and to help proteasomal protein degradation in axon terminals in  vivo (Liu et  al. 2019). Furthermore, the axonal swelling and accumulation of ubiquitinated proteins at the axonal terminals around DCN neurons precede degeneration of PI31-knockout PCs (Minis et al. 2019). These findings suggest that the UPS plays a role in the maintenance of axon function and survival of PCs. Aggregates of mutant proteins are observed in the cerebellar neurons of postmortem patients and animal models of several SCAs (Seidel et al. 2012). Moreover, it was found that proteasomal components are frequently recruited to these aggregates in cellular and mouse models of SCAs (Chai et al. 1999; Cummings et al. 1998; Seki et  al. 2007). Additionally, genetic mutations in the C-terminus of Hsc70-­ interacting protein (CHIP), another ubiquitin ligase, have been identified as

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etiologies of SCA48 and spinocerebellar ataxia, autosomal recessive 16 (SCAR16) (Genis et al. 2018; Shi et al. 2013). Since CHIP mediates the ubiquitination for the degradation of aggregate-prone proteins, it markedly contributes to protein quality control by the UPS (Jiang et al. 2001). Kanack et al. recently demonstrated that various CHIP missense mutations found in SCAR16 commonly destabilize CHIP (Kanack et al. 2018). Therefore, a decline in protein quality control by the UPS may be involved in the pathogenesis of cerebellar ataxia. However, age-related decline of proteasomal activity is not observed in the mouse cerebellum, which is in contrast to that found in other brain regions, including the cerebral cortex, hippocampus, and spinal cord (Keller et al. 2000). Therefore, it remains unclear whether UPS impairment is the main etiology of cerebellar ataxia.

3.3 Autophagy-Lysosome Pathways in Cerebellar Purkinje Cells Neurodegeneration in the cerebellum is frequently observed in lysosomal storage disorders (LSDs), which are caused by a deficiency of lysosomal enzymes (Platt et al. 2012). Prominent degeneration of cerebellar PCs and severe motor impairment are observed in patients and animal models of Niemann-Pick disease type C (NPC), which is caused by the genetic loss of NPC1 or NPC2 that leads to the characteristic accumulation of cholesterol in lysosomes (Tang et al. 2010). LSDs are accompanied by decreased activity of ALP-mediated protein degradation (Settembre et al. 2008). Therefore, these results suggest that PCs are vulnerable to ALP impairment. The importance of MA (one of the pathways in the ALP) in PCs was first demonstrated in mice with PC-specific knockout of autophagy-related gene 7 (Atg7), an MA-related protein (Komatsu et al. 2007). Swelling of axon terminals and slight loss of PCs were observed at postnatal day 56 of the Atg7-knockout mice, while dendritic arbors and motor function were not affected. These results suggest that MA is more strongly related to the maintenance of axon function than to the regulation of survival and dendritic morphology of PCs. Additionally, MA impairment is observed in cellular and animal models of SCAs (Alves et al. 2014; Onofre et al. 2016), suggesting MA impairment might be commonly related to the pathogenesis of cerebellar ataxia. A recent report revealed that PC-specific knockdown of cathepsin D, a lysosomal proteolytic enzyme involved in all three pathways of the ALP, resulted in the greater impairment of the survival of PCs than PC-specific knockdown of Atg7 (Koike et al. 2017). Therefore, CMA or mA may have greater contribution to the survival of PCs than MA. In line with this finding, miRNA-mediated knockdown of LAMP2A, a CMA-related protein, in cerebellar neurons triggers neurodegeneration of PCs and other cerebellar neurons and progressive motor impairment, whereas knockdown of tumor susceptibility gene 101 protein, an mA-related protein, does not affect motor function in mice (Sato et  al. 2021). Additionally, several SCA-causing proteins

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commonly impair CMA (Seki et  al. 2012, 2018a), indicating that CMA plays a pivotal role in the survival of cerebellar PCs and that CMA impairment is involved in the common pathogenesis of cerebellar ataxia. Several studies have revealed that SCA-causing proteins commonly induce shrinkage of PC dendrites in cerebellar primary cultures (Irie et al. 2014; Ohta et al. 2021; Seki et al. 2009, 2018a). A similar phenotype has also been reported in cultured PCs differentiated from SCA6 patient-derived induced pluripotent stem cells (Ishida et al. 2016). Dendritic shrinkage of PCs precedes PC degeneration and is related to the onset of motor dysfunction in several SCA mouse models (Shakkottai et  al. 2011; Watanave et  al. 2019). Therefore, these findings suggest dendritic shrinkage of cultured PCs might be an in vitro phenotype commonly observed in various types of SCAs. Furthermore, similar dendritic shrinkage of PCs was observed in cultured PCs treated with a lysosomal inhibitor at low concentration (Seki et  al. 2018a), suggesting that the ALP regulates the morphology of PC dendrites. As described above, the impairment of ALP disrupts the survival and morphology of PCs. Conversely, several reports indicates that an excessive MA contributes to PC neurodegeneration. A transient ischemia induces MA activation and PC degeneration in juvenile rats (Au et al. 2015). This PC degeneration is inhibited by the siRNA-mediated knockdown of Atg7, an MA-related protein (Au et al. 2015), suggesting the involvement of the excessive MA in PC degeneration. An excessive MA is also observed in PCs of Purkinje cell degeneration (pcd) mice prior to PC neurodegeneration (Chakrabarti et al. 2009). Pcd mice exhibit adult-onset degeneration of PCs that is caused by the deletion mutation of Nna1 gene (Fernandez-­ Gonzalez et  al. 2002), a zinc carboxypeptidase localized in the nucleus and cytoplasm (Harris et al. 2000). Although there is no evidence indicating the relationship between Nna1 and MA, Nna1 cleaves peptides generated from proteasome-­ mediated proteolysis to amino acids and is related to protein turnover (Berezniuk et  al. 2010). Additionally, an enhancement of MA is also observed in tbl mice described above (Mashimo et al. 2009). HERC1 mediates the degradation of tuberous sclerosis complex 2, which negatively regulates the activity of mammalian target of rapamycin (mTOR) (Chong-Kopera et  al. 2006). Since the inhibition of mTOR potently activates MA (Mizushima and Levine 2020), the elevation of TSC2 caused by the loss of function of HERC1 in tbl mice triggers MA activation. Motor training decreases MA activity and prevents PC degeneration (Fucà et  al. 2017), supports the importance of MA activation in PC degeneration. Since many autophagosomes in PCs contain mitochondria in pcd mice (Chakrabarti et al. 2009), aberrant or enhanced mitophagy (MA-mediated degradation of mitochondria) might be involved in the PC degeneration triggered by MA activation. These findings suggest that the disturbance of ALP activity affects the morphology and survival of PCs and causes cerebellar ataxia. Figure 5 summarizes molecular mechanisms in PC degeneration and PC axonal dysfunction by the dysregulation of UPS and ALP.

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Fig. 5 Molecular mechanisms in PC degeneration and PC axonal dysfunction by the dysregulation of UPS and ALP

4 Endogenous Modulators of Purkinje Cells 4.1 Thyrotropin-Releasing Hormone (TRH) Although TRH is a hypothalamic hormone that enhances the release of thyroid-­ stimulating hormone from the pituitary gland, it is also distributed in extra-­ hypothalamic brain regions, including the cerebellum, and may function as a neurotransmitter or neuromodulator (Shibusawa et  al. 2008). Both TRH and its orally available analog, taltirelin, exert an anti-ataxic effect on 3-AP-induced ataxic rats and several types of natural mutant animals showing the ataxic phenotype (Kinoshita et al. 1995, 1998; Muroga et al. 1982; Nakamura et al. 2005). Therefore, these chemicals have been approved for the clinical treatment of cerebellar ataxia (Kinoshita et al. 1995; Sobue et al. 1983). However, the detailed molecular mechanism of the anti-ataxic effect of TRH has not been fully elucidated. Histological studies have revealed that the TRH receptor (type 2) is expressed in granule cells and interneurons of the cerebellar cortex but not in PCs (Sun et al. 2000), indicating that TRH has no direct effect on PCs. Moreover, NMDA receptors were found to mediate the anti-ataxic effect of taltirelin in 3-AP-treated rats (Kinoshita et  al. 1998). Since the TRH receptor is also expressed in inferior olive neurons in the medulla (Sun et al. 2000), the anti-ataxic effect of TRH might be mediated by glutamate released from the climbing fibers. Recently, Watanave et  al. reported that TRH is involved in motor learning (Watanave et al. 2018). Although cerebellar morphology is not affected, LTP at synapses between PCs and parallel fibers is diminished by TRH-knockout mice. Furthermore, treatment with TRH rescues motor

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learning deficits and loss of LTP in TRH-knockout mice, and this rescue is mediated by nitric oxide (NO) and the subsequent generation of cyclic guanosine monophosphate (cGMP) (Watanave et al. 2018). Since neural NO synthase (nNOS) is mainly expressed in granule cells and interneurons of the cerebellum (Vincent and Kimura 1992), TRH may stimulate NO production in granule cells and interneurons, followed by transsynaptic activation of guanylate cyclase and increase in cGMP in PCs.

4.2 Thyroid Hormones (THs) THs (3,3′,5-triiodothyronine [T3], and thyroxine [T4]) regulate neuronal migration, differentiation, and axonal myelination during the postnatal development of the cerebellum (Faustino and Ortiga-Carvalho 2014). The knockout of TH-related genes has proved the importance of THs in cerebellar development. Impaired development of PC dendrites and motor incoordination are observed in paired box gene 8-knockout mice, which are hypothyroid because of the malformation of the thyroid gland (Horn et al. 2013), and in mice lacking monocarboxylate transporter 8 and organic anion transporting polypeptide 1c1, which are involved in the transport of THs through the blood-brain barrier (Mayerl et al. 2014). THs bind to nuclear TH receptors (TRs) and enhance the expression of genes involved in cerebellar development, including nerve growth factor, brain-derived neurotrophic factor (BDNF), and retinoid receptor-related orphan receptor α (RORα) (Koibuchi and Iwasaki 2006). Among them, RORα is abundant in cerebellar PCs and closely related to cerebellar development. Staggerer mice, a natural mutant in which the RORα gene is mutated (Hamilton et al. 1996), show tremor, motor incoordination, and impaired development of PC dendrites (Herrup and Mullen 1981; Sidman et  al. 1962). Genetic deletion of RORα triggers phenotypes similar to those observed in staggerer mouse (Dussault et al. 1998), suggesting that loss of function of RORα impairs the development of cerebellar PCs in staggerer mice. A decrease in RORα in cerebellar PCs was also observed in the SCA model transgenic mice (Konno et al. 2014; Serra et  al. 2006). Additionally, miRNA-mediated knockdown of RORα triggers atrophy of PC dendrites, decreased PC survival, and motor impairment in adult mice (Yasui et al. 2021). These reports suggest that RORα plays a pivotal role in the maintenance of dendritic morphology and survival of mature PCs as well as their development. THs enhance TR-mediated transcription from promoters containing TH response elements (TREs) (Koibuchi and Iwasaki 2006). Although RORα is one of the target genes of THs, it also interacts with TR and enhances TH-mediated transcription from promoters containing TRE (Koibuchi et al. 1999). Additionally, BDNF is not upregulated during cerebellar development in staggerer mice (Qiu et  al. 2007). Therefore, these findings demonstrate that RORα is crucial for TH-mediated development of the cerebellum.

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4.3 Glutamate Receptor δ2 (GluRδ2) and D-Serine Lurcher mice are natural mutant mice characterized by an ataxic phenotype and loss of multiple neurons around the cerebellum, including PCs, granule cells, interneurons in the molecular layer, and inferior olive neurons in the medulla (Phillips 1960; Zanjani et  al. 2006). The Lurcher phenotype is caused by missense mutation in GluRδ2 (Zuo et al. 1997), which is an orphan receptor similar to ionotropic glutamate receptors and is predominantly expressed at the dendritic spines of cerebellar PCs (Yuzaki 2004). The mutation found in Lurcher mice constitutively activates GluRδ2, leading to the apoptosis of PCs during the postnatal development of the cerebellum (Zuo et  al. 1997). However, hotfoot mice, whose GluRδ2 genes are deleted, and GluRδ2-knockout mice only show a mild ataxic phenotype (Kurihara et al. 1997; Lalouette et al. 1998). Loss of GluRδ2 does not trigger degeneration of PCs but it impairs the formation and stabilization of synapses between PCs and parallel fibers and the monoinnervation of PCs by climbing fibers (Yuzaki 2004). Therefore, GluRδ2 regulates synaptic formation and the survival of PCs during postnatal development of the cerebellum. Additionally, an antibody against the extracellular domain of GluRδ2 impaired the induction of LTD in cerebellar slices. Moreover, this antibody also triggered a transient ataxic phenotype in adult mice when it was injected into subarachnoid supracerebellar space (Yuzaki 2004). These findings suggest that GluRδ2 also plays an essential role in the regulation of synaptic plasticity in PCs of the mature cerebellum. Kakegawa et  al. identified D-serine as an endogenous ligand of GluRδ2 (Kakegawa et al. 2011). Exogenous treatment with D-serine reduces AMPA receptor expression at PC-parallel fiber synapses through GluRδ2-induced endocytosis of AMPA receptors (Kakegawa et al. 2011). D-serine is generated from L-serine by serine racemase, mainly in astrocytes, and acts as a co-agonist of NMDA receptors through the glycine-binding site (Schell et  al. 1997). Moreover, the reduction in D-serine in patients with schizophrenia (Hashimoto et al. 2003) suggest that D-serine acts as an endogenous gliotransmitter that regulates NMDA receptor function. Although serine racemase is expressed in the Bergmann glia of cerebellum (Wolosker et al. 1999), the amount of D-serine in the adult cerebellum is lower than that in the forebrain (Miyoshi et  al. 2012; Schell et  al. 1997). This is due to the cerebellarspecific expression of D-amino acid oxidase (DAO), a metabolic enzyme of D-serine (Kim et  al. 2019; Shibuya et  al. 2013). Since the expression of DAO gradually increases after birth and peaks at 4  weeks of age (Shibuya et  al. 2013), D-serine exists in the cerebellum only during the early postnatal period. D-serine colocalizes with NMDA receptors at the PC dendrites in the immature cerebellum (Schell et al. 1997). Furthermore, NMDA receptors are reported to be present in the cerebellum only during the developmental period (Garthwaite et al. 1987), and these receptors are important for the synaptic elimination of PCs from climbing fibers to establish monoinnervation to PCs by climbing fibers (Green et  al. 1992). Lastly, D-serine contributes to the induction of LTD via GluRδ2  in the immature cerebellum (Kakegawa et al. 2011). These results suggest that D-serine participates in synaptic formation through both NMDA receptors and GluRδ2 during cerebellar development.

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4.4 Nitric Oxide (NO) NO synthase (NOS) is more abundant in the cerebellum than in other brain regions (Förstermann et al. 1990). Therefore, NO may play a crucial role in cerebellar function. In line with this idea, motor coordination deficits have been reported in mice lacking nNOS (Kriegsfeld et al. 1999), indicating that nNOS is involved in cerebellar function. nNOS is mainly expressed in granule cells and interneurons in the molecular layer but not in PCs (Vincent and Kimura 1992). However, NO is related to both LTP and LTD induction in PCs (Daniel et al. 1998; Kakizawa et al. 2012). Therefore, NO derived from the axonal terminals of parallel fibers and interneurons transmits and regulates intracellular signals in PCs. In LTD induction, NO activates guanylate cyclase and increases intracellular cGMP, followed by activation of protein kinase G (PKG). Activation of both PKG, along with the activation of protein kinase C (PKC) by the stimulation of metabotropic glutamate receptor 1 (mGluR1), then induces LTD at the synapses between PCs and parallel fibers (Daniel et  al. 1998). In LTP induction, NO activates type 1 ryanodine receptors (RyR1) via its S-nitrosylation and induces Ca2+ release from the endoplasmic reticulum (Kakizawa et al. 2012). The nNOS-derived NO also regulates the dendritic morphology of PCs. Knockout of nNOS increases dendrite thickness, reduces mature dendritic spines, and decreases the expression of mGluR1 in mature PCs (Tellios et al. 2020). Although vesicular glutamate transporter 1, which is localized at the presynaptic terminals of parallel fibers, is not affected in nNOS-knockout mice, synaptic responses from parallel fibers are reduced because of the decrease in mGluR1, which is involved in the synaptic responses from parallel fibers (Yamasaki et al. 2021). Moreover, synaptic terminals from climbing fibers are increased in nNOS-knockout mice (Tellios et al. 2020). These findings indicate that nNOS-derived NO modulates the balance of neural inputs between parallel and climbing fibers in PCs. Additionally, nNOS is specifically involved in the survival of primary cultured PCs, while other types of NOS are not (Oldreive et al. 2012). A decrease in mGluR1 signaling is frequently observed in animals and patients with SCA (Yamasaki et al. 2021). Additionally, a positive allosteric modulator of mGluR1 improved the motor performance of SCA1 model mice (Notartomaso et al. 2013). In contrast, the receptor activity is enhanced by missense mutations of mGluR1 that are identified in patients with SCA44 (Watson et al. 2017). Therefore, the disturbance of mGluR1 signaling is related to the pathogenesis of cerebellar ataxia. nNOS-derived NO might have an important role in regulating mGluR1 signaling in PCs during the pathogenesis of cerebellar ataxia. As described above, NO plays an essential role in regulating synaptic transmission, morphology, and survival under physiological conditions. This is supported by the findings that show NOS activity and nitrotyrosine levels are strongly increased in the cerebellum of Lurcher mice (McFarland et al. 2007). Since reactive nitrogen is toxic to primary cultured PCs (Oldreive et al. 2012), oxidative stress by excessive NO may contribute to the degeneration of PCs in Lurcher mice. However,

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deficiency of nNOS does not affect PC degeneration and nitrotyrosine formation (McFarland et al. 2007), indicating that other types of NOS trigger excessive NO production in Lurcher mice. Since reactive microglia accumulate in the cerebellum of Lurcher mice (Cairns et al. 2017), inducible NOS (iNOS) in reactive microglia would be responsible for excessive NO production in Lurcher mice. Microglial activation is also frequently observed in various animal models of cerebellar ataxia as described in Sect. 2.3. Therefore, excessive NO generated from iNOS may be involved in the pathogenic mechanisms of cerebellar ataxia. Acute ethanol intake triggers transient cerebellar ataxia, characterized by loss of motor coordination and dysarthria, in human and animal models (Saeed Dar 2015). Ethanol rapidly decreases nNOS amount in rat cerebellum (Auta et  al. 2020). Inactivation of nNOS by ethanol would lead to excessive activation of PCs and GABA-mediated suppression of neurons in DCN (Saeed Dar 2015). Taken together, both excess and deficiency of NO in the cerebellum impairs PC functions and triggers ataxic phenotype.

4.5 Hydrogen Sulfide and D-Cysteine Hydrogen sulfide has recently been investigated as an endogenous gasotransmitter similar to NO (Kimura 2021). It modifies protein function via the persulfidation of cysteine residues and regulates various neural functions, including LTP. Additionally, hydrogen sulfide enhances the persulfidation of free L-cysteine and glutathione and increases anti-oxidative and neuroprotective activities (Paul and Snyder 2018). Hydrogen sulfide is endogenously generated from L-cysteine through four different pathways: cystathionine γ-lyase (CSE), cystathionine β-synthase (CBS), cysteine aminotransferase/3-mercaptopyruvate sulfurtransferase (3MST) (Kamoun 2004), and cysteinyl-tRNA synthetase (Akaike et al. 2017). Decrease in these enzymes and hydrogen sulfide production are associated with several neurodegenerative diseases, including Parkinson’s disease, Huntington’s disease, and Alzheimer’s disease (Paul and Snyder 2018). Conversely, the administration of hydrogen sulfide or its donors provides a protective effect in the experimental models of neurodegenerative diseases (Kida et al. 2011; Xie et al. 2013). Among the hydrogen sulfide-generating enzymes, CBS is related to cerebellar morphology and function. It is more abundantly expressed in the cerebellum than in other brain regions (Enokido et al. 2005). Studies have reported that patients with CBS deficiency present with severe progressive polymyoclonus and ataxia (Awaad et  al. 1995). Additionally, the cerebellum is markedly smaller in CBS-knockout mice (Enokido et  al. 2005). Similarly, the Wistar Kyoto rat, an animal model of depression, shows reduced expression of CBS along with a smaller cerebellum (Nagasawa et al. 2015). These findings suggest that CBS is involved in the development and function of the cerebellum. As for the other hydrogen sulfide-generating enzymes, CSE shows reduced expression in patients with SCA3, while

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overexpression of CSE rescues the ataxic phenotype of SCA3 model Drosophila (Snijder et al. 2015). Shibuya et al. revealed a novel pathway for the generation of hydrogen sulfide from D-cysteine (Shibuya et al. 2013). In this pathway, D-cysteine is converted to 3-mercaptopyruvate (3MP) by DAO, followed by the generation of hydrogen sulfide from 3MP by 3MST. As described in Sect. 4.3, DAO is selectively expressed in the cerebellum (Kim et al. 2019). Hydrogen sulfide is more effectively and selectively generated from D-cysteine in the cerebellum than in other brain regions (Snijder et al. 2015). Therefore, D-cysteine could be a novel hydrogen sulfide donor that is selective to the cerebellum. D-cysteine enhances the dendritic development of PCs via the production of hydrogen sulfide (Seki et al. 2018b). Both DAO and 3MST are reported to be expressed in astrocytes (Ono et al. 2009; Zhao et al. 2013). Since CBS and CSE are also expressed in Bergmann glia (Enokido et  al. 2005; Snijder et  al. 2015), hydrogen sulfide in the cerebellum is mainly generated in Bergmann glia and transactivates PCs, leading to the enhancement of dendritic development. D-cysteine ameliorates dendritic shrinkage in primary cultured PCs expressing several types of SCA-causing proteins (Ohta et al. 2021). Additionally, it suppresses the onset of motor dysfunction, neurodegeneration, and glial activation in SCA1 model mice (Ohta et  al. 2021). These results indicate the potential of hydrogen sulfide supplementation as a therapeutic strategy for cerebellar ataxia, along with D-cysteine as a preventive drug for SCAs. Among the various D-amino acids, only D-serine and D-aspartate are generated and functional in mammalian tissues (Genchi 2017). Earlier, D-cysteine was neither considered present nor produced in mammals. However, Semenza et  al. recently reported that D-cysteine is endogenously generated by serine racemase in the mouse brain, where brain D-cysteine concentration is highest during the embryonic period (Semenza et al. 2021). During this period, D-cysteine regulates the proliferation and differentiation of neural precursor cells (Semenza et al. 2021). D-cysteine is also present in the adult mouse brain, especially in the forebrain regions, where DAO is not expressed (Ono et al. 2009). Since serine racemase is expressed in Bergmann glia (Wolosker et al. 1999), hydrogen sulfide might be constitutively generated in Bergmann glia from D-cysteine, which is converted from L-cysteine by serine racemase, and contributes to the physiological functions in the cerebellum. Figure 6 summarizes molecular mechanisms how endogenous factors regulate survival, morphology, and synaptic plasticity of PCs.

5 Concluding Remarks Increasing evidence about the regulation of survival and morphology of cerebellar PCs provides the possible common mechanisms for cerebellar ataxia: (1) dysregulation of synaptic inputs from other neurons, (2) inflammatory glial activation, (3) dysregulation of ALP-mediated proteolysis, (4) dysregulation of NO production.

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Fig. 6  Molecular mechanisms how endogenous factors regulate survival, morphology, and synaptic plasticity of PCs

The establishment of the methods to regulate these mechanisms will contribute to developing novel therapeutic strategies for cerebellar ataxia. Acknowledgment  I would like to thank Editage (www.editage.com) for English language editing.

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Genetics of Dominant Ataxias Ashraf Yahia and Giovanni Stevanin

Abstract  Autosomal dominant cerebellar ataxia (ADCA) accounts for more than fifty inherited neurological diseases caused by various underlying mechanisms leading to progressive or intermittent phenotypes. Most ADCAs develop due to alterations in signal transduction, ion homeostasis, or pathological DNA expansions. Yet, other pathological mechanisms can directly or indirectly contribute to the development of these diseases. Initially, ADCA diagnosis was dominated by linkage and nucleotide repeat expansion analyses. However, the advent of next-generation sequencing has enhanced ADCA diagnosis, including the discovery of novel forms due to nucleotide repeat expansions and conventional mutations, with limited reliable genotype–phenotype correlations within the major ADCA subgroups. The developments in ADCA diagnosis have outpaced the discovery of effective treatments and biomarkers for these diseases, particularly the progressive neurodegenerative subtypes. Multiple clinical trials are, however, underway with some promising results but there are still many challenges to overcome. Keywords  Cerebellar ataxia · Nucleotide repeat expansion · Channelopathies · Genetic diagnosis · Polyglutamine expansion

A. Yahia Department of Biochemistry, Faculty of Medicine, University of Khartoum, Khartoum, Sudan Institut du Cerveau – Paris Brain Institute, ICM, Sorbonne University, INSERM, CNRS, APHP, Paris, France G. Stevanin (*) Institut du Cerveau – Paris Brain Institute, ICM, Sorbonne University, INSERM, CNRS, APHP, Paris, France INCIA, CNRS, Bordeaux University, EPHE, Bordeaux, France e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_4

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Abbreviations ADCA ADCADN CAPOS

Autosomal dominant cerebellar ataxia Autosomal dominant cerebellar ataxia, deafness, and narcolepsy Cerebellar ataxia, areflexia, pes cavus, optic atrophy, and sensorineural hearing loss CNV Copy number variation DHA Docosahexaenoic acid DRPLA Dentatorubral-pallidoluysian atrophy EA Episodic ataxia EEAT1 Excitatory amino acid transporter-1 EGFR Epidermal growth factor receptor ER Endoplasmic reticulum NGS Next-generation sequencing NMDA N-methyl-D-aspartate NPTX1 Neuronal pentraxin 1 protein/gene RAN Repeat-associated non-AUG SCA Spinocerebellar ataxia SCA42ND SCA42 with neurodevelopmental deficits SPTBN2 Spectrin beta non-erythrocytic-2 protein UTR Untranslated region WES Whole exome sequencing WGS Whole genome sequencing

1 Introduction Ataxia refers to a neurological condition characterized by poorly coordinated movements. Autosomal dominant forms of cerebellar ataxias (ADCAs) encompass >50 clinically and genetically heterogeneous disorders (Klockgether et al. 2019; Giunti et al. 2020). Their estimated world-wide prevalence is 2.7/100,000 in most populations (Ruano et al. 2014) but in some regions can reach higher values because of founder effects, such as in the province of Holguin in Cuba (47.9/100,000) (Velázquez-Pérez et  al. 2020) or in the islands of the Azores (39/100,000) (De Araújo et al. 2016). Most ADCAs fall within the spinocerebellar ataxia (SCA) subgroup, followed in frequency by episodic ataxias (EAs). SCAs usually have a progressive course due to degeneration of the cerebellum and/or its connections. These involvements are reflected in their core presentation of gait ataxia and incoordination, dysarthria, and oculomotor problems (Klockgether et  al. 2019; Müller 2021). In more complex SCA forms, other structures of the nervous system degenerate as well, particularly the brainstem (Sullivan et al. 2019; Müller 2021). At the other end of the spectrum, EAs are characterized by intermittent attacks of cerebellar dysfunction of variable

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duration, sometimes accompanied by other paroxysmal neurological conditions, such as epilepsy, migraine, and dystonia (Giunti et al. 2020; Harvey et al. 2021). Pathologically, most ADCA patients suffer from the consequences of nucleotide repeat expansions or, to a lesser extent, channelopathies. However, other disease mechanisms are directly implicated in the pathogenesis of some ADCA subtypes. These include abnormal gene expression or alterations of lipids metabolism, endolysosomal/autophagy pathway, mitochondria, and energy metabolism (Matilla-­ Dueñas et al. 2014; Klockgether et al. 2019; Sullivan et al. 2019). The successive identification of seven nucleotide repeat expansion-linked dominant ataxias in the nineties, and a few more in recent years, and their involvement in almost half of all patients, have for a long time focused attention on these particular mutations (Durr 2010; Krygier and Mazurkiewicz-Bełdzińska 2021). The report of point mutations in dominant ataxias is more recent (Krygier and Mazurkiewicz-Bełdzińska 2021). Table 1 lists the different ADCA subtypes and their mutational mechanisms.

2 Genetic Diagnosis Linkage analysis and candidate gene analyses have dominated the discovery of novel ADCA subtypes for decades (Yahia and Stevanin 2021). However, during the past decade, next-generation sequencing (NGS)-based approaches have been taking over. NGS is becoming more and more efficient due to the ongoing improvement in its associated algorithms that enable the detection of a wide spectrum of variations, including DNA repeat expansions and deletions (Chintalaphani et al. 2021). Of particular interest are the recent advances in long-read genomic sequencing (e.g., Oxford Nanopore and PacBio technologies) and optical mapping methods (e.g., Bionano), as they can unravel disease-causing structural variants that may explain part of the missing heritability in ataxias (Yahia and Stevanin 2021). In the clinics and other large-scale settings, except in patients with episodic presentation of ataxia, most ADCA patients are initially screened for repeat expansions using PCR-based approaches in view of the frequency of the repeat-associated subtypes (Cagnoli et al. 2018; Kang et al. 2019; Velázquez-Pérez et al. 2020; Bogdanova-­ Mihaylova et al. 2021; Riso et al. 2021). The detection rate for repeat expansion screening depends on several factors including, among others, the origin of the studied patients, the age at disease onset, the velocity of disease progression, the type and number of genes screened, and the presence of an anticipation of the age at onset. For example, in a Cuban cohort with dominant ataxia, the detection rate for repeat expansion screening was 90.8% (881/970) and 87% of the screened cases had a repeat expansion in ATXN2 (Velázquez-Pérez et al. 2020). The detection rates in two large Asian cohorts were 62.5% (932/1489) in a Chinese cohort and 52% (131/265) in a Thai cohort, with SCA3 as the most common subtype (Boonkongchuen et al. 2014; Choubtum et al. 2015; Chen et al. 2018). In a smaller Japanese cohort (34/52), SCA6 was the most common genetic entity with an overall 65% diagnostic yield (Sakakibara et al. 2017). On the other hand, the detection rate of these SCAs

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Table 1  List of autosomal dominant cerebellar ataxia subtypes, their causative genes, and mutational mechanisms Disease SCA1

Gene ATXN1

OMIMa # 164400

SCA2

ATXN2

# 183090

SCA3

ATXN3

# 109150

SCA4 SCA5

SPTBN2

% 600223 # 600224

SCA6

CACNA1A

# 183086

EA2 Early onset progressive ataxia SCA7 SCA8

# 108500

ATXN7

# 164500

ATXN8OS ATXN8

# 608768

SCA9 SCA10

ATXN10

% 612876 # 603516

SCA11

TTBK2

# 604432

SCA12

PPP2R2B

# 604326

SCA13

KCNC3

# 605259

SCA14

PRKCG

# 605361

SCA15/16

ITPR1

# 606658

SCA29 SCA17

TBP

# 117360 # 607136

KCND3

% 607458 # 607346

TMEM240

# 608687 # 607454

SCA18 SCA19/ SCA22 SCA20 SCA21

Mutational mechanism CAG/polyglutamine expansion CAG/polyglutamine expansion CAG/polyglutamine expansion Point mutation CAG/polyglutamine expansion Point mutation Point mutation

CAG/polyglutamine expansion Untranslated and CAG/polyglutamine expansions Untranslated repeat expansion Point mutation Untranslated repeat expansion Point mutation

Reference Banfi et al. (1994) Pulst et al. (1996) Kawaguchi et al. (1994) Hellenbroich et al. (2003) Ikeda et al. (2006) and Perkins et al. (2016) Zhuchenko et al. (1997) and Du et al. (2013) Ophoff et al. (1996) Yue et al. (1997) and Tonelli et al. (2006) David et al. (1997) Koob et al. (1999) Moseley et al. (2006) Higgins et al. (1997) Matsuura et al. (2000) Houlden et al. (2007) and Bowie and Goetz (2020) Holmes et al. (1999)

Waters et al. (2006) and Khare et al. (2017) Point mutation Chen et al. (2003) and Wong et al. (2018) CNV and rarely point Van De Leemput et al. (2007) mutation Point mutation Huang et al. (2012) CAG/polyglutamine Koide et al. (1999) expansion Brkanac et al. (2002) Point mutation Lee et al. (2012) and Hsiao et al. (2019) Knight et al. (2008) Point mutation Delplanque et al. (2014) and Seki et al. (2018) (continued)

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Table 1 (continued) Disease SCA23

Gene PDYN

OMIMa # 610245

SCA25

PNPT1

# 608703

SCA26 SCA27 EA-like

EEF2 FGF14

# 609306 # 609307

SCA28

AFG3L2

# 610246

SCA30 SCA31

BEAN1

% 613371 # 117210

SCA34

ELOVL4

# 133190

SCA35

TGM6

# 613908

SCA36

NOP56

# 614153

SCA37

DAB1

# 615945

SCA38 SCA39 SCA40 SCA41 SCA42 SCA42ND SCA43 SCA44 SCA45 SCA46 SCA47 SCA48 SPAX1 GRID2

ELOVL5

# 615957

CCDC88C TRPC3 CACNA1G

# 616053 # 616410 # 616795 # 618087 # 617018 # 617691 # 617769 # 617770 # 617931 # 618093 # 108600

MME GRM1 FAT2 PLD3 PUM1 STUB1 VAMP1 GRID2

 # 620158

SCA50 KCNA2 DRPLA

NPTX1 KCNA2 ATN1

# 125370

ADCADN

DNMT1

# 604121

CAPOS

ATP1A3

# 601338

Mutational mechanism Point mutation

Reference Bakalkin et al. (2010) and Smeets et al. (2015, 2020) Point mutation Stevanin et al. (2004) and Barbier et al. (2022) Point mutation Hekman et al. (2012) Point mutation Van Swieten et al. (2003), Tempia et al. (2015), and Piarroux et al. (2020) Point mutation, CNV Di Bella et al. (2010) and Pareek and Pallanck (2020) Storey et al. (2009) Untranslated repeat Sato et al. (2009) expansion Point mutation Cadieux-Dion et al. (2014) and Sherry et al. (2017) Point mutation Wang et al. (2010) and Tripathy et al. (2017) Untranslated repeat Kobayashi et al. (2011) expansion Seixas et al. (2017) Untranslated repeat expansion Point mutation Di Gregorio et al. (2014) Large 11q duplication Johnson et al. (2015) Point mutation Tsoi et al. (2014) Point mutation Fogel et al. (2015) Point mutation Coutelier et al. (2015a) Chemin et al. (2018) Point mutation Depondt et al. (2016) Point mutation Watson et al. (2017) Point mutation Nibbeling et al. (2017) Point mutation Nibbeling et al. (2017) Point mutation Gennarino et al. (2015, 2018) Point mutation Genis et al. (2018) Point mutation Bourassa et al. (2012) Point mutation Kumagai et al. (2014) and Coutelier et al. (2015b) Point mutation Coutelier et al. (2021) Point mutation Helbig et al. (2016) CAG/polyglutamine Koide et al. (1994) expansion Point mutation Winkelmann et al. (2012) and Maresca et al. (2020) Point mutation Demos et al. (2014) and Tranebjærg et al. (2018) (continued)

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Table 1 (continued) Disease EA1

Gene KCNA1

OMIMa # 160120

Mutational mechanism Point mutation

EA3 EA5

CACNB4

% 606554 # 613855

Point mutation

EA6

SLC1A3

# 612656

Point mutation

EA7 EA8 EA9

SCN2A

% 611907 % 616055 # 618924

Point mutation

SCA49

SAMD9L

# 611170

Point mutation

Reference Browne et al. (1994) and Zhao et al. (2020) Steckley et al. (2001) Escayg et al. (2000) and Subramanyam et al. (2009) Jen et al. (2005) and Winter et al. (2012) Kerber et al. (2007) Conroy et al. (2014) Liao et al. (2010) and Schwarz et al. (2016) Corral-Juan et al. (2022)

OMIM online Mendelian inheritance in man catalog (https://omim.org/), SCA spinocerebellar ataxia, EA episodic ataxia, ADCADN autosomal dominant cerebellar ataxia, deafness, and narcolepsy, CAPOS cerebellar ataxia, areflexia, pes cavus, optic atrophy, and sensorineural hearing loss a OMIM entry number preceded by “%” indicates that the entry describes a confirmed phenotype for which the underlying molecular mechanism is not known, “#” indicates Mendelian phenotypes caused by known genes

was relatively low (13.8%, 11/80) in a cohort from Australia (Kang et al. 2019) and intermediate (42.8%, 12/28) in a cohort from Ireland (Bogdanova-Mihaylova et al. 2021). Generally, the relative frequencies of these SCAs is the result of past human migrations and the existence of founder effects in certain populations, such as SCA3 in Portugal and Asia (Martins et al. 2007; Martins and Sequeiros 2018), SCA2 in Cuba (Velázquez Pérez et al. 2009), SCA10 in South America (Almeida et al. 2009), SCA36 in Galicia (Spain) (Arias et al. 2017), and SCA12 in India (Bahl et al. 2005). Overall SCA3 is the major form in most countries but there are exceptions, e.g., Cuba and Italy (Klockgether et al. 2019). Most of the time, because of their higher frequencies, only SCAs due to coding CAG repeat expansions are analyzed, but the diagnostic yield can be further enhanced by screening more genes for non-coding DNA repeat expansions (Choubtum et  al. 2015; Chen et  al. 2018) and enhanced even more using NGS-­ based approaches in negative cases to search for conventional variants (Galatolo et al. 2021). An NGS panel-based approach achieved a genetic diagnosis in 14.3% (59/412) of ADCA patients with negative results on DNA repeat expansion screening (Coutelier et al. 2017). A similar percentage, 13.5% (13/96), was obtained in another cohort after negative DNA repeat expansion screening (Nibbeling et  al. 2017). The use of NGS-based genome-wide approaches, whole exome sequencing (WES) and whole genome sequencing (WGS), further enhance diagnostic success, with the potential for discovering novel causative genes or known genes in overlapping syndromes (Nibbeling et al. 2017; Kang et al. 2019; Ngo et al. 2020; Yahia and Stevanin 2021). This last point is illustrated by the recent identification of

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heterozygous variants by WGS in PNPT1 as responsible for SCA25 (Barbier et al. 2022), while this gene was previously reported in an autosomal recessive multisystem disorder with mitochondrial dysfunction (MIM#614932). Indeed, WGS is emerging as a promising first-tier test in rare diseases in general (Turro et al. 2020). Recent evidence showed its high sensitivity and specificity in detecting repeat expansions (Ibañez et  al. 2022), advocating for its integration in routine ADCA diagnosis, possibly as the near future gold-standard genetic test. A limiting factor is the availability and economic feasibility of WGS which differ between countries. If WGS sequencing is not feasible, the choice of genetic tests for ADCA diagnosis is dictated by: tests’ availability, patients’ origin as discussed earlier, and the clinical context. An example for how clinical context could prioritize the diagnostic approach is that while screening for repeats expansion is advised in patients with progressive adult-onset ADCA (De Silva et al. 2019), NGS-based approaches (targeted gene panels and WES) or array genotyping are preferred in childhood-onset ADCA with negative family history and even in adult patient with very low progression rate (Brandsma et al. 2019).

3 ADCA Pathological Mechanisms 3.1 Abnormal Repeat Expansions To date, 13 ADCAs are known to be associated with abnormal expansion of DNA repeats (Krygier and Mazurkiewicz-Bełdzińska 2021). Abnormal CAG repeats are located in protein-coding regions in seven ADCAs, SCA1, SCA2, SCA3, SCA6, SCA7, SCA17, and dentatorubral-pallidoluysian atrophy (DRPLA), known collectively as CAG/polyglutamine SCAs (Banfi et  al. 1994; Kawaguchi et  al. 1994; Koide et al. 1994, 1999; Pulst et al. 1996; David et al. 1997; Zhuchenko et al. 1997). In addition, in SCA8, an abnormal CTG expansion is transcribed in both directions; in the sense strand it is located in the 3′ UTR of the gene, while in the anti-sense strand it is translated as CAG repeats and then into polyglutamine-expanded peptides when mutated (Koob et al. 1999; Moseley et al. 2006). In SCA12, the abnormal CAG repeat expansion is located in the 5′ untranslated region of the PPP2R2B gene (Holmes et al. 1999). In four other SCAs, the abnormal expansions are located in introns: SCA10, SCA31, SCA36, and SCA37 (Matsuura et al. 2000; Sato et al. 2009; Kobayashi et  al. 2011; Seixas et  al. 2017). Generally, when the disease-­ causing expansions are located outside coding exons, they tend to be much larger than those located in coding exons (Klockgether et  al. 2019). Both exonic and intronic expansions are dynamic and their sizes vary between patients from the same family (Klockgether et al. 2019), objectified as somatic and germline instability. The presence of nucleotide interruptions in the repeat sequence may, however, stabilize the sequence and, consequently, instability is then rare in some cases, such as in SCA17. The repeat size affects the severity and age at onset of the disease in

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many SCA subtypes; this contributes to the phenomenon of anticipation, more particularly in polyglutamine SCAs (Klockgether et al. 2019). In addition to the size of the pathological allele, normal alleles of some dominant ataxia genes, or variants of DNA repair genes influence the age at onset and disease severity (Du Montcel et al. 2014; Bettencourt et al. 2016). The pathophysiology of polyglutamine SCAs differs depending on the functions and the interactions of the native proteins but they share a general mechanistic theme (Malik et al. 2021). The altered proteins in polyglutamine SCAs are directly or indirectly toxic to the cells (Klockgether et al. 2019; Malik et al. 2021). Direct toxicity to the cell comes from the altered proteins forming toxic compounds, losing their own functions, or sequestering other proteins beneficial to the cell (Klockgether et al. 2019; Malik et al. 2021). In addition, abnormal repeats can be translated in all six frames and both sense and antisense, the so-called repeat associated non-AUG (RAN) translation, which reinforces the toxic function hypothesis although this has not been demonstrated in all these pathologies (Zu et  al. 2011). Indirect toxicity comes as a consequence of the direct toxicity by placing a continuous burden on the transcriptional and translational machinery in the cell (Klockgether et  al. 2019; Niewiadomska-Cimicka et  al. 2020). For example, most of the genes causing polyglutamine-­SCAs encode proteins vital for regulating gene expression, which may explain part of the commonalities observed (Niewiadomska-Cimicka et  al. 2020). For these reasons, multiple systems are affected in the cells and one of the most promising therapeutic strategy nowadays seems to reduce the expression of the pathological proteins at the RNA level using RNA interference or antisense oligonucleotides, before the toxic properties surpass the cell plasticity (Brooker et al. 2021). Intronic repeats of SCAs cause cellular toxicity by sequestering RNA-binding proteins, affecting epigenomic processes such as RNA splicing and gene expression or forming toxic RNA aggregates (White et al. 2012; Seixas et al. 2017; Klockgether et  al. 2019; Malik et  al. 2021). RAN translation has also been implicated in the pathological process of some clinico-genetic entities. In the case of SCA12, overexpression of the PPP2R2B transcript in a size-dependent manner seems to be involved (O’Hearn et al. 2015).

3.2 Channelopathies and Alteration of the Signal Transduction Pathways Neurons are particularly sensitive to alterations of the cellular functions ensuring their specific role in cell–cell communication. This includes channels important for ion homeostasis and action potential transmission, but also synaptic machinery dynamics (receptors, structural proteins, etc.), particularly glutamate signalling.

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3.2.1 Specific Ion Channel Alterations More than 140 genes encode voltage-gated ion channels (Alexander et al. 2019). Ion channels are important in the nervous system as they are pivotal for neuronal development, neurotransmission, action potentials, and other neuronal processes (Spillane et al. 2016; Smith and Walsh 2020). Genetic disorders of the ion channels can manifest prenatally, early in life, or after adulthood and can be episodic or progressive (Spillane et al. 2016; Noebels 2017; D’Adamo et al. 2020). Patients with channelopathies variably present with ataxia, epilepsy, migraine, brain malformations, and other neurological manifestations (Spillane et al. 2016; D’Adamo et al. 2020). The phenotypic spectrum of genetic channelopathies reflects the nature and vintage of the brain circuits where each particular channel acts (Noebels 2017; Smith and Walsh 2020). Channelopathies are caused by mutations that alter the channel function or mutations that affect its expression, post-translational modifications, trafficking/targeting, or assembly (Spillane et al. 2016; Noebels 2017). More rarely, some mutations disturb processes not directly related to the channel function, such as sequestering of other proteins (Khare et  al. 2017). Mutations in voltage-­ gated and ligand-gated potassium, sodium, and calcium channels and transporters cause some subtypes of ADCA. EA1, SCA13, and SCA19 are caused by mutations in the genes encoding the voltage-gated potassium channel subunits Kv1.1, Kv3.3, Kv4.3, respectively (Browne et al. 1994; Waters et al. 2006; Lee et al. 2012). Of note, SCA13 may not be a pure channelopathy as the p.R423H variant of the Kv3.3 channel indirectly sequesters the receptor of the epidermal growth factor (EGFR), possibly disturbing the EGFR-NOTCH morphogenetic signaling pathway, adding another layer of complexity to the SCA13 phenotype (Khare et al. 2017). Abnormal calcium channels and transportation are direct causes of six subtypes of ADCAs. Mutations in the alpha Cav2.1 and alpha Cav3.1 subunits of the voltage-­ gated calcium channel cause EA2 and both SCA42 and SCA42 with neurodevelopmental deficits (SCA42ND), respectively, while mutations in the calcium channel voltage-dependent subunit beta-4 cause EA5 (Ophoff et  al. 1996; Yue et  al. 1997; Escayg et al. 2000; Coutelier et al. 2015a; Chemin et al. 2018). SCA15/SCA16 and SCA29 are caused by mutations in the ITPR1 gene encoding inositol 1,4,5-­triphosphate (IP3) receptor, an intracellular IP3-gated calcium channel that modulates intracellular calcium signaling (Van De Leemput et al. 2007; Huang et al. 2012). Altered sodium trafficking causes only one ADCA type compared to potassium and calcium: EA9 is due to mutations in the SCN2A gene encoding the sodium voltage-gated channel alpha subunit-2 (Liao et al. 2010). 3.2.2 Alteration of the Synaptic Machinery Channels and transporters regulating neurotransmitter function are also implicated in the pathogenesis of some ADCAs. Glutamate is the most abundant neurotransmitter in the brain and the one of the most involved in the pathogenesis of ADCAs

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(Reiner and Levitz 2018). SCA41 is caused by mutations in the TRPC3 gene, which encodes a nonselective cation channel highly expressed in Purkinje cells and linked to signaling pathways, including metabotropic glutamate receptor-dependent synaptic transmission (Fogel et al. 2015). Similarly, SCA44 and GRID2-linked ataxia are caused by mutations in GRM1 and GRID2, encoding glutamate metabotropic receptor-1 and glutamate ionotropic receptor delta type subunit-2, respectively (Kumagai et al. 2014; Coutelier et al. 2015b; Watson et al. 2017). EA6 is caused by mutations in the SLC1A3 gene encoding a glial glutamate transporter, the excitatory amino acid transporter-1 (EEAT1) (Jen et al. 2005). EEAT1 belongs to a family of transporters that regulate the concentration of neurotransmitters at excitatory glutamatergic synapses (Jen et  al. 2005). Another member of this family, EEAT4, is indirectly implicated in the pathogenesis of SCA5, which is caused by mutations in the gene SPTBN2 encoding spectrin beta non-erythrocytic-2 protein (SPTBN2) (Ikeda et al. 2006; Perkins et al. 2016). SPTBN2 stabilizes the Purkinje cell-specific glutamate transporter EAAT4 at the plasma membrane (Perkins et  al. 2016). Recently, point mutations in the gene encoding neuronal pentraxin 1 (NPTX1) were shown to affect the protein stability and/or its secretion, which may affect its role in synapse dynamics (Coutelier et  al. 2021). Finally, the STUB1-encoded protein is involved in N-methyl-D-aspartate (NMDA) receptor degradation (Ferreira et  al. 2013) and accounts for recessive (SCAR16) and dominant (SCA48) ataxias (Shi et al. 2013; Genis et al. 2018), with the dominant form presenting at an earlier age and giving rise to a more severe phenotype (Roux et al. 2020).

3.3 Abnormal Gene Expression Regulation of gene expression and other epigenetic processes is important for the developing and the mature brain (Starr 2019). Epigenetic regulators include chromatin and histone modifiers, mRNA regulators, and components of the transcription and the translation machinery (Starr 2019; Aygun and Bjornsson 2020). The autosomal dominant cerebellar ataxia, deafness, and narcolepsy syndrome (ADCADN) is caused by mutations in the DNA methyltransferase-1, a chromatin modifier encoded by the DNMT1 gene (Winkelmann et al. 2012; Maresca et al. 2020). Similarly, pathogenic mutations in a Purkinje cell transcription regulator, α1ACT, contribute to SCA6 (Du et  al. 2013). This transcript is encoded by CACNA1A, a bicistronic gene that is involved in EA2 through the other transcript (Ophoff et al. 1996; Du et al. 2013). Similarly, SCA47 is caused by mutations in the PUM1 gene encoding a post-transcriptional regulator of gene expression that binds to the PUM recognition element in the 3′-untranslated region (UTR) of target mRNAs (Gennarino et al. 2015, 2018; Wolfe et al. 2020). Finally, impaired translation is also implicated in ADCA pathogenesis, as illustrated by SCA26, caused by mutations in the EEF2 gene encoding eukaryotic translation elongation factor-2 (Hekman et al. 2012).

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3.4 Disorders of Lipid Metabolism Disorders of lipid metabolism are implicated in many neurodevelopmental and neurodegenerative conditions (Hussain et  al. 2019; Darios et  al. 2020; Grassi et  al. 2020). Indeed, lipids are major molecular players in the nervous system. Three ADCA subtypes are caused primarily by defects in the metabolism of fatty acids and phospholipids. SCA34 and SCA38 are caused by mutations in the ELOVL4 and ELOVL5 genes, encoding elongation of very-long-chain-fatty-acids enzymes (Aldahmesh et  al. 2011; Cadieux-Dion et  al. 2014; Di Gregorio et  al. 2014). Similarly, SCA46 is caused by mutations in the PLD3 gene encoding the phospholipase D3 enzyme, important for the hydrolysis of phospholipids (Selvy et al. 2011; Nibbeling et al. 2017).

3.5 Other Mechanisms Additional mechanisms contribute to the pathogenesis of ADCAs either directly or indirectly. Lysosomal and ER dysfunctions are directly implicated in the development of SCA21 and SCA35, respectively (Tripathy et  al. 2017; Seki et  al. 2018). TMEM240 encodes a trans-membrane protein that induces lysosomal damage when mutated, leading to morphological changes in Purkinje cells of SCA21 animal models (Seki et al. 2018). Of note, the endolysosomal pathway also contributes to the pathogenesis of SCA6 (Unno et  al. 2012). On the other hand, SCA35 involves aberrant activation of the unfolded protein response due to mutations in TGM6, which encodes the transglutaminase 6 protein (Tripathy et  al. 2017). The point mutations in the neuronal pentraxin 1 gene (NPTX1) were shown to induce an ER stress through its retention, which may contribute partially to the disease (Coutelier et al. 2021), as in the case of some KCND3 variants (Duarri et al. 2012). Finally, the gene encoding polyribonucleotide nucleotidyltransferase PNPase 1 (PNPT1) was found to be mutated in SCA25 patients, thereby linking cerebellar syndrome to mitochondrial DNA processing, a situation known in recessive ataxias (Barbier et al. 2022). Another important mitochondrial protein is a metalloprotease involved in maintenance of the mitochondrial proteome and which is composed of homodimers of AFG3L2 or heterodimers with paraplegin. Variants in the gene encoding AFG3L2 have been implicated in SCA28 (Di Bella et al. 2010), while variants in the gene encoding paraplegin are responsible for a frequent autosomal recessive spastic ataxia (Coarelli et  al. 2019). Interestingly, heterozygous variants in the gene encoding paraplegin are suspected of contributing to late onset ataxia (Klebe et al. 2012).

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4 Phenotype–Genotype Correlations The cardinal difference between the two main ADCA subgroups is the progressive disease course in SCAs and the intermittent nature of EAs. Nevertheless, some EA patients develop persistent cerebellar features (Graves et  al. 2014). On the other hand, there are very few distinctive phenotypic features that could predict the mutated gene within the different ADCA subgroups (Fig. 1). Dermal anomalies are recurrently seen in SCA34 (Bourque et al. 2018). Macular degeneration is still the best predictor of SCA7; however, since the clinical presentation depends on the size of the repeat, the age at examination and the presence of additional modifiers, this sign is not always present and has occasionally been reported in other forms such as SCA2 (Rufa et al. 2002; Michalik et al. 2004; Park et al. 2020). In addition, there are no distinctive ancillary or biochemical markers except serum docosahexaenoic acid (DHA) in SCA38 (Di Gregorio et al. 2014). The constant reporting of new cases enlarges the phenotypic spectrum of each genetic entity so that they tend to overlap each other. This is well illustrated by the multiple clinico-genetic entities in which cerebellar ataxia and pyramidal features, also known as spastic ataxias, are associated. This clinical overlap also extends to other neurogenetic diseases; i.e., some cases with CAG repeat expansions in the

Fig. 1  Clinical signs that feature prominently with cerebellar ataxia in different autosomal dominant cerebellar ataxia subtypes. SCA spinocerebellar ataxia, EA episodic ataxia, ADCADN autosomal dominant cerebellar ataxia, deafness, and narcolepsy, CAPOS cerebellar ataxia, areflexia, pes cavus, optic atrophy, and sensorineural hearing loss

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ATXN2 gene may have a cerebellar syndrome or a Parkinson-like phenotype (Charles et al. 2007), or be at risk for amyotrophic lateral sclerosis (Lattante et al. 2014) according to the CAA/CAG repeat size and its composition, a rare case of phenotype–genotype correlation in ADCA.  Indeed, CAG repeat/polyglutamine SCAs are the only ADCA subgroup where phenotype–genotype correlations have been clearly established between the size of the repeat and the age at onset, and in some cases the clinical presentation. Another example is the retinopathy that only occurs in SCA7 patients with medium or large expansion size. This is far less evident for conventional variants although the nature of the variants may play a role in the considerable phenotypic variability in the allelic SCA15/SCA16 and SCA29 diseases due to ITPR1 deletions versus point mutations, respectively. Their clinical presentations can then extend from non-progressive congenital cerebellar ataxia with delayed motor development in SCA29 or in Gillespie syndrome when associating visual impairment and hypotonia, to adult onset slowly progressive ataxia with hyperreflexia (SCA15) (MIM#147265). A better understanding of the different phenotypic presentations according to the variants may lead to better follow-up for patients regarding the onset of specific signs such as vision loss or dementia. Interestingly, even if conventional mutations only represent a few percent of cases (Coutelier et al. 2017), a comparison of the clinical profiles of patients with conventional mutations or CAG repeat expansions showed that the evolution is significantly slower and less severe in the former (Monin et al. 2015). This information sometimes helps to guide the genetic diagnosis, prioritizing or not the quest for repeat expansions at first.

5 Biomarkers and Treatment The developments in genetic diagnostic approaches have enhanced the diagnosis of ADCAs and related genetic diseases (Yahia and Stevanin 2021). However, these improvements in ADCA diagnosis have far outpaced the development of therapies for these conditions and the identification of biomarkers to assess their severity and response to treatments. As with most neurogenetic conditions, there is no cure for cerebellar degeneration and its symptoms, or for the detrimental effect on patient’s quality of life. However, the battery of promising pharmacological, non-­ pharmacological, and novel gene therapy interventions to manage ADCA is expanding. Generally, pharmacological and non-pharmacological interventions are symptom-oriented such as L-Dopa for responsive Parkinsonism, or preserving motor coordination with exercises and exergames, respectively (Ilg et  al. 2012; Ayvat et al. 2022). On the other end, gene therapy-based interventions aim to modify the disease etiology itself. Overall, EAs cause less disability compared to SCAs and have more therapeutic options. Most therapeutic interventions in EAs are phenotype-driven rather than genotype-driven (Silveira-Moriyama et al. 2018). However, some exceptions exist; for example, fampridine, a potassium channel blocker, and acetazolamide are

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effective in reducing the number of attacks in EA2 and some other EAs (Muth et al. 2021). Regarding SCAs, there is no disease-modifying treatment yet; however, several promising medications are at different stages of clinical trials (Brooker et al. 2021). The interest on riluzole, a potent neuroprotective molecule through its modulation of excitatory transmission in SCAs and Friedreich ataxia patients (Romano et al. 2015), is debated in light of recent results raising doubts about its efficacy in SCA2 patients (Coarelli et al. 2022). Antisense oligonucleotides (ASOs) are a promising therapeutic approach that has been tested in ADCA animal and cell models (Hauser et al. 2021; Zhang et al. 2021). ASOs are short synthetic oligonucleotides designed to target specific RNAs to modulate their functions through multiple mechanisms depending on their chemistry, sequence, and target (Silva et al. 2020). These mechanisms include inhibiting protein synthesis machinery, modifying splicing, interfering with RNA capping and tailing, and interfering with microRNA-directed RNA regulation (Silva et al. 2020). Most strategies are targeting specific exonic sequences outside the repeat, but with consequences possibly on the expression of the normal allele except when targeting allele specific polymorphisms in cis of the expansion. Other strategies are targeting the repeat itself. The effect of these strategies on the toxic species produced by RAN translation has not been explored yet, however. Interestingly, clinical trials to evaluate ASO for treating Huntington’s disease, another repeat expansion disorder, have started with promising initial data (Tabrizi et al. 2019; Arnold 2021; Ghanekar et al. 2022) but with very disappointing results at present (Kingwell 2021). A second promising gene therapy-based approach is correcting pathogenic genetic alterations through viral vectors. There are multiple viral vectors and the choice depends on the disease and the modality of cell targeting, in vivo versus ex vivo (Li and Samulski 2020). Adeno-associated virus vector has been approved for treating neurological and eye diseases (Li and Samulski 2020), thus promising for treating ADCA. Another promising approach for ADCA treatment is the clustered regularly interspersed short palindromic repeats (CRISPR)-Cas9 directed gene editing technique. This technique has successfully corrected disease-causing mutations in several models of polyglutamine diseases (Karwacka and Olejniczak 2022), including patients-­ derived fibroblasts (He et al. 2021), and animal models (Yang et al. 2017). However, further studies are required to establish the safety and efficacy of CRISPR-Cas9 gene editing in treating ADCA and overcome its limitations, particularly the off-­ target issue. An interesting therapeutic intervention that we also want to highlight is the substitution of deficient substances and metabolites for treating ADCA. An example is the administration of oral DHA for treating SCA38 (Manes et  al. 2017, 2019). SCA38 is caused by mutations in ELOVL5 encoding an enzyme involved in the synthesis of polyunsaturated fatty acids, including docosahexaenoic acid (Di Gregorio et al. 2014). Also, DHA is used as a biomarker for the disease (Di Gregorio et al. 2014). Indeed, the lack of adequate reliable biomarkers for most forms is still a major hurdle to treating numerous SCAs. There are only a few non-specific predictors for many SCAs, including clinical, serological, digital, radiological, and physiological

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predictors (Klockgether et al. 2019; Kim et al. 2021). Age, CAG repeat length, and the associated clinical features, e.g., double vision in SCA3, are important classical predictors for individuals at risk of SCA to develop ataxia (Jacobi et  al. 2020). Electrophysiological predictors were suggested for some SCA subtypes, e.g., multifocal electroretinogram for SCA1 and F-wave in SCA3 (Cai et al. 2020; Ziccardi et al. 2021) as well as MRI-based biomarkers (Joers et al. 2018; Jacobi et al. 2020). Serum neurofilament light chain is a severity predictor for several SCAs and correlates with their degree of disability (Coarelli et  al. 2021; Shin et  al. 2021). Interestingly, a recent study detected serum neurofilament light chain changes in the pre-ataxic stage of SCA1 subjects and preceded volumetric MRI alterations, suggesting a more prominent role for this biomarker in the future (Wilke et al. 2022). Wearable sensors have been used as digital biomarkers to estimate motor impairment in SCA patients, including those in their pre-SCA prodromal period (Shah et al. 2021; Velázquez-Pérez et al. 2021).

6 Conclusions The genetic and clinical heterogeneity of ADCA is a real challenge in diagnosis. Even if NGS and long-read sequencing are now reducing diagnostic wandering, the high frequency of undiagnosed patients, either because no variant was found or because the variants identified were inconclusive, is still a real issue in daily practice. This calls for international collaborative sharing of information on variants and for the use of the latest diagnostic strategies, such as the SOLVE-RD (https://solve-­ ­rd.eu/) and ATAXIA-Global (https://ataxia-­global-­initiative.net/) initiatives. Since ADCA is quite diverse in clinical and genetic origin, the identification of common therapeutic strategies for multiple entities is challenging. There is a cross-­ talk between the mechanisms involved that hinders a discrete delineation between the involved pathological mechanisms (Klockgether et al. 2019). For example, EAs and SCAs are not restricted to one category of gene function. In addition, other mechanisms are indirectly implicated in ADCAs’ pathogenesis, sometimes as secondary events, e.g., abnormal neuronal development, abnormal signal transduction, and others, further complicating such a delineation (Sullivan et al. 2019; Malik et al. 2021). In polyglutamine SCAs, multiple cell pathways are affected concomitantly. Most hope has then been placed on the development of RNA targeting-based strategies in polyglutamine SCAs, because of the convincing results in animal models and the fact that these strategies can target the disease process very early. It is likely that pharmacological compounds will also be identified in the future, in particular to target signal transduction and channel ADCAs. Alternative strategies using molecules targeting translation to help truncating variants escape the non-sense-­ mediated mRNA decay may potentially benefit some patients, but the delivery of these molecules to the brain and the absence of toxicity are issues that will need to be solved.

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Other challenges exist, including the low prevalence of these conditions and their phenotypic and genetic heterogeneity (Brooker et al. 2021). The variability of phenotypic presentation is largely understudied. Multicentric collaborations and animal models may be beneficial for overcoming some of these obstacles (Lin et al. 2020; Cendelin et al. 2021). Acknowledgments  We are grateful to Alexis Brice and Nick Barton for critical reading of the manuscript. Funding Information  The authors’ work is financially supported by ATAXIA-UK (to GS), the European Union’s Horizon 2020 research and innovation program under grant agreement No. 779257 (to GS), and the association Connaître les Syndromes Cérébelleux (to GS).

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Autosomal and X-Linked Degenerative Ataxias: From Genetics to Promising Therapeutics Anya Hadji, Aurélie Louit, Vincent Roy, Mathieu Blais, François Berthod, François Gros-Louis, and Nicolas Dupré

Abstract  Autosomal recessive cerebellar ataxias (ARCAs) refer to a large group of neurodegenerative disorders mainly affecting the cerebellum and the nervous system. ARCAs are characterized by important genetic heterogeneity and complex phenotypes. Because of their rarity and heterogeneity, it is challenging to rapidly advance our understanding in addition to discovering viable symptomatic and, most importantly, disease-modifying treatments. Significant advances have been recently achieved regarding the genetic basis of autosomal recessive and X-linked cerebellar ataxias. Unfortunately, the pathophysiology of most ARCAs is poorly characterA. Hadji · A. Louit LOEX, CHU de Quebec-Université Laval Research Center, Quebec, QC, Canada V. Roy LOEX, CHU de Quebec-Université Laval Research Center, Quebec, QC, Canada Department of Surgery, Faculty of Medicine, Université Laval, Quebec, QC, Canada M. Blais LOEX, CHU de Quebec-Université Laval Research Center, Quebec, QC, Canada Regenerative Medicine Axis, CHU de Québec-Université Laval Research Center, Quebec, QC, Canada F. Berthod · F. Gros-Louis LOEX, CHU de Quebec-Université Laval Research Center, Quebec, QC, Canada Department of Surgery, Faculty of Medicine, Université Laval, Quebec, QC, Canada Regenerative Medicine Axis, CHU de Québec-Université Laval Research Center, Quebec, QC, Canada N. Dupré (*) LOEX, CHU de Quebec-Université Laval Research Center, Quebec, QC, Canada Department of Medicine, Faculty of Medicine, Université Laval, Quebec, QC, Canada Neuroscience Axis, CHU de Québec-Université Laval Research Center, Quebec, QC, Canada e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_5

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ized. For most ARCAs, clinical management consists in supplying symptomatic treatments. However, many new therapeutic strategies have emerged. They range from reducing the debilitating effects of ARCAs to exploring curative strategies. The aim of this chapter is to discuss fundamental and novel genetic aspects of ARCAs and X-linked cerebellar ataxias, focusing specifically on the fragile X tremor ataxia syndrome (FXTAS). We summarize clinical features, pathophysiology, diagnosis, currently available therapies, and novel research for the most frequent ARCAs. We also present examples of how novel and cutting-edge therapeutic tools including the clustered regularly interspaced short palindromic repeats (CRISPR) approach, antisense oligonucleotides (ASOs), and stem cells may lead to disease-modifying and ultimately curative treatment for ARCAs. The emphasis is made on new and ongoing research for the most frequent ARCAs. We will discuss promising future therapeutic strategies as well. Keywords  Recessive ataxia · FXTAS · Friedreich’s ataxia · Ataxia with oculomotor apraxia · Ataxia with vitamin E deficiency · Ataxia-telangiectasia · ARSACS

1 Introduction Autosomal recessive cerebellar ataxias (ARCAs) and X-linked cerebellar ataxias form a large group of neurodegenerative disorders affecting the cerebellum and the nervous system. Recessive disorders are inherited when an individual has two copies of a mutated gene, including homozygous mutations as well as compound heterozygous mutations. X-linked disorders are the result of mutated genes located on the X chromosome and can be either recessive or semidominant (Zanni and Bertini 2018). ARCAs now consist of more than 40 distinct clinical entities and an even greater number of genes associated with these disorders (Embirucu et  al. 2009). Most patients present with highly variable phenotypes, which are leading to an important clinical heterogeneity, even when bearing the same mutations. One of the most prevalent ARCAs, namely Friedreich ataxia (FRDA), affects approximately 1/50,000 individuals with the same male/female ratio (Koenig 2003). Other ARCAs, such as autosomal-recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) were reported worldwide but are more frequent in certain regions due to a founder effect (Bouchard et al. 1998). It is possible that other ARCAs are yet to be discovered because of their rareness. ARCAs are generally early-onset diseases that are commonly diagnosed before the age of 20 (Palau and Espinos 2006). The clinical presentation of ARCAs often consists of slowly progressive neurological symptoms including gait disturbances, poor coordination of eye movements, speech alteration, and reduced hand dexterity (Kwei and Kuo 2020). In some cases, systemic symptoms can be observed, such as heart disease in FRDA (Hanson et al. 2019). Clinicians must generally consider family history, clinical findings, neuroimaging, and molecular diagnosis to establish the

Autosomal and X-Linked Degenerative Ataxias: From Genetics to Promising Therapeutics 143 Table 1  Currently ongoing clinical trials on autosomal and X-linked degenerative ataxias Diseases Friedreich ataxia

Ataxia telangiectasia

Clinicaltrials.gov study identifier NCT03933163 NCT04801303 NCT04577352 NCT04102501 NCT02255435 NCT04870866 NCT03309150

Treatment or drug Resveratrol Calcitriol Vatiquinone RT001 (11,11-di-deutero-linoleic acid ethyl ester) Omaveloxolone Nicotinamide ribonucleoside M6620 (inhibitor of ataxia telangiectasia and Rad3 related (ATR) kinase) monotherapy or in combination with carboplatin or paclitaxel

Clinical trial phase Phase 2 Phase 4 Phases 2 and 3 Phase 3 Phase 2 Phase 2 Phase 1

diagnosis because of the heterogeneity of ARCAs (Bird 1993). The diversity and complexity of ARCAs are also a challenge to their treatment. Hopefully, in recent years, advances in molecular gene edition, like the clustered regularly interspaced short palindromic repeats (CRISPR) technique, have given great hope to find new gene therapies that will eventually prevent the devastating effects of cerebellar ataxias. In this chapter, we will provide an overview of clinical features, pathophysiology, and diagnostic criteria for ARCAs. These include FRDA, ARSACS, synaptic nuclear envelope protein 1 (SYNE1)-related ataxia, and ataxia telangiectasia (AT). Less frequent ataxias will also be considered, including ataxia with oculomotor apraxia type 1 (AOA1) and type 2 (AOA2), spastic paraplegia type 7 (SPG7), ataxia with vitamin E deficiency (AVED), and FXTAS, which is included in the X-linked progressive (or degenerative) ataxias group of disorders. Promising therapeutic strategies will also be presented. Emphasis will be placed on clinical therapies, as well as on currently ongoing clinical trials (Table  1) and new research on future therapeutic options for the most frequent ARCAs as inspired by cutting-edge approaches with dominant forms of ataxias.

2 Classification Establishing a classification of ARCAs is challenging because of its complex phenotype and high genetic heterogeneity. Up to recently, there was a serious need for a consensual classification of ARCAs that would facilitate clinical diagnosis. Two classifications were recently proposed to the scientific community. The first classification was led by the Society for Research on the Cerebellum Ataxias and involved a Task Force of 11 neurologists (Beaudin et al. 2017, 2019). This classification was established based on clinical symptoms and pathophysiological features, providing a unified understanding of autosomal recessive cerebellar disorders for clinicians and researchers. It includes the classification of 59 disorders (Beaudin et al. 2019). The second one was proposed by the International Parkinson and Movement Disorder Society Task Force and their classification includes 62 disorders with

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ataxia as the predominant feature (Rossi et al. 2018). It proposes a new nomenclature for the genetically confirmed ARCAs to guide the molecular diagnostic testing and to facilitate its interpretation.

3 Friedreich Ataxia (FRDA) 3.1 Clinical Features Friedreich’s ataxia (MIM#229300) is the most common inherited ataxia (Koenig 2003). The gradient prevalence of FRDA in Europe colocalizes with the chromosomal R1b marker discovered in the same region. Two main reasons could explain this gradient: Paleolithic migrations out of the Franco-Cantabrian Ice Age refuge or Neolithic migrations into west Europe with the spread of agriculture (Vankan 2013) This may be one of the reasons explaining that this disease is uncommon in Sub-­ Saharan African groups (Labuda et al. 2000) and mostly affects Caucasian groups. The average age of disease onset is 15 years old but, cases of late-onset and very-­ late-­onset have been observed in individuals between 25 and 40 years old (Cook and Giunti 2017). There is an important diversity of clinical phenotypes. FRDA always involves gait and limb ataxia, dysarthria, and loss of lower limb reflexes (Cook and Giunti 2017; Koeppen 2011). There are also systemic effects related to this disease such as cardiomyopathy, diabetes, and skeletal abnormalities (Holt et  al. 2019). Children suffering of FRDA may appear clumsy, and present gross and fine motor difficulties in comparison to nonaffected siblings. Ataxia may be preceded by scoliosis and pes cavus (Koeppen 2011). Also, cardiomyopathy might be one of the first clinical manifestations in some patients, while diabetes mellitus is always a late complication (Koeppen 2011). The most common cause of death in FRDA is cardiac dysfunction, namely, congestive heart failure or arrhythmia, and the average age at death was reported as 36.5 years old (range of 12–87 years old) in a large retrospective study (Cook and Giunti 2017; Tsou et al. 2011).

3.2 Pathophysiology FRDA is caused by genetic alteration to the FXN gene, which encodes a highly conserved 220 amino acid mitochondrial protein called frataxin (Santos et al. 2010). The FXN gene span of 95  kb of the genomic sequence contained in 7 exons is located on the long arm of chromosome 9 (9q13-21.1) and is characterized by a homozygous expansion of guanine-adenine-adenine (GAA) trinucleotide in the first intronic region. People affected by this disease can present up to 1300 GAA repeats, with an average of 400 repeats, while healthy individuals generally have fewer than 36 trinucleotide repetitions. However, about 2–4% of patients have heterozygous mutations, which result in atypical phenotypes (Cossee et al. 1999).

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Repeat expansions are inherently unstable (dynamic) and share numerous genetic features, they: (1) arise from normally existing polymorphic repeats; (2) often change size when transmitted to next generations; (3) tend to cause more severe and earlier onset disease when longer (Delatycki and Bidichandani 2019; Filla et  al. 1996); and (4) present variable phenotype, primarily reflecting differences in repeat size. Homozygous patients for the expansion have marked reduced levels of frataxin (Li et al. 2016). A small fraction of FRDA patients are compound heterozygotes, that is, bearing a single GAA expansion on one allele coupled with a deletion or loss-of-function mutation on the second allele (Cossee et al. 1999). Frataxin is a 14.2 kDa mitochondrial protein involved in the regulation of iron transport and the biosynthesis of the iron-sulfur cluster (ISC). In fact, iron homeostasis disruption is also associated with deficiency in proteins containing ISC cofactors like mitochondrial respiratory complexes, Krebs cycle proteins, DNA repair, and replication proteins (Holt et al. 2019). Frataxin is mostly expressed in the dorsal root ganglia (DRG), spinal cord, cerebellar dentate nuclei, cerebral cortex, pancreas, heart, liver, and skeletal muscle (Cossee et al. 1997), which correspond to the main pathological sites associated with FRDA. Most of the neurological manifestations originate from the DRG, dentate nuclei of the cerebellum, posterior columns, spinocerebellar and corticospinal tracts of the spinal cord and peripheral nerves (Koeppen 2011). In addition, lymphoblasts FXN transcripts and protein levels were found to be 5–30% higher in affected individuals compared to healthy people (Chutake et al. 2014).

3.3 Diagnosis and Treatment The diagnosis is generally established by clinical examination, and it is confirmed by molecular genetic testing. A large fiber sensory axonal neuropathy can be revealed in nerve conduction investigations. However, the absence of neuropathy does not exclude the diagnosis (Collins 2013). Early diagnosis and treatment are important for neurodegenerative disorders like FRDA and it explains why we need to identify specific biomarkers. A study showed that epigenetic modifications, particularly miRNA-based regulatory mechanisms, might be linked to FRDA (Viswambharan et al. 2017). This function is currently being investigated. In fact, Frataxin expression was found to be affected by genetic variations producing miRNA target sites in the 3′-UTR of FXN (Viswambharan et  al. 2017). To date, there is no cure for FRDA and symptomatic treatments may include multidisciplinary considerations because FRDA is a multisystem disorder (Lynch et  al. 2021a). As musculoskeletal complications are common, physiotherapy is often required as well as surgery to treat scoliosis. Various clinical scales are used to evaluate the progression of the disease, such as the Friedreich’s Ataxia Rating Scale, the International Cooperative Ataxia Rating Scale, and the Scale for the Assessment and Rating of Ataxia.

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3.4 Current Clinical Research At least two therapeutic avenues have been investigated with more relative success (Cook and Giunti 2017). The first strategy focuses on the management of the cellular oxidative stress caused by the mitochondrial dysfunction. In FRDA, oxidative stress was suggested to be undermined by the inability to efficiently activate the NF-E2 p45-related factor 2 (Nrf2) pathway (Paupe et al. 2009). Defects in the Nrf2 pathway have been described in several in vitro and in vivo models of FRDA and have been associated with the mitochondrial impairment and the oxidative imbalance (Paupe et al. 2009; Lynch and Farmer 2021; D’Oria et al. 2013). Many antioxidants are being investigated as a treatment for FRDA. Of particular interest, the benzoquinone idebenone is an antioxidant that has been widely explored for its potential to alleviate symptoms associated with frataxin deficiency and it has been shown to be safe and well tolerated in humans (Lagedrost et al. 2011; Lynch et al. 2010; Meier et al. 2012). Idebenone medication enhances echocardiographic parameters in patients with FRDA, such as hypertrophy (Ribai et al. 2007). However, conflicting results exist regarding its potential benefits regarding cardiomyopathy (Giovanni et  al. 2015). Indeed, randomized double-blind placebo-­controlled trials have failed to establish any robust evidence of benefit for neurological or cardiac function (Lagedrost et al. 2011; Lynch et al. 2010; Meier et al. 2012). A clinical pilot study to determine the efficacy, safety, and tolerability of triple therapy with deferiprone, idebenone, and riboflavin in FRDA patients has also concluded that there is an uncertain benefit on the neurological and heart functions of this triple therapy in FRDA, as measured by changes in the scale for the assessment and rating of ataxia (SARA) and echocardiography parameters (Arpa et al. 2014). Omaveloxolone, an Nrf2 activator combined with a nuclear factor kappa B (NF-­ κB) suppressor that prevents the ubiquitination of Nrf2 and thus decreases its turnover, was shown to improve neurological function (Lynch et  al. 2021b) and is currently in clinical trial phase 2 (NCT02255435). Another phase 2 clinical trials are currently underway, involving resveratrol (NCT03933163), also studied in autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS). An improvement in neurological function in FRDA patients with high-dose administration was reported (Yiu et al. 2015). A Phase 3 clinical trial on Vatiquinone, a drug targeting an oxidoreductase involved in the synthesis of an essential compound playing a role in the control of oxidative stress is also underway (NCT04577352). The previous Phase 2 clinical trial (NCT01962363) resulted in an improved neurological function and disease progression (Zesiewicz et al. 2018a). Also, a (Phase 3) clinical trial on RT001, a lipid peroxidation inhibitor is underway (NCT0410250). RT001 is a deuterated ethyl linoleate. It showed significant improvements in maximal exercise workload, giving patients hope to overcome severe fatigue during task performance (Zesiewicz et al. 2018b). Finally, Britti and collaborators reported that calcitriol, an active form of vitamin D physiologically synthesized by mitochondria, increased the level of mature frataxin in DRG neurons, frataxin-deficient cardiomyocytes, and

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in lymphoblastoid cell lines derived from FRDA patients, leading to improved mitochondrial function and cell survival (Britti et al. 2021). This promising approach is currently being studied (Phase 4 clinical trial) (NCT04801303). The second strategy focuses on re-establishing the iron homeostasis. Indeed, iron accumulation is a hallmark feature of FRDA and, initially, iron accumulation was suggested to be a primary pathogenic event triggered by FXN deficiency (reviewed in Llorens et al. (2019)). In cellular models, the iron chelator deferiprone has been shown to pass through the blood–brain barrier and to promote the elimination of intracellular iron (Pandolfo and Hausmann 2013; Boddaert et  al. 2007). Despite encouraging safety results (Elincx-Benizri et al. 2016) and documented benefit to cardiac health of FRDA patients (Pandolfo and Hausmann 2013), choosing the optimal dosage is very challenging (Crisponi et al. 2015). In addition, depletion of mitochondrial iron was demonstrated to induce mitophagy, which may increase the risk of adverse effect (Hara et al. 2020).

4 Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) 4.1 Clinical Features ARSACS (MIM#270770) was first described in the Charlevoix and Saguenay-Lac-­ St-Jean regions (astern Canada), where it is one of the most common inherited ataxias due to a founder effect. Cases of ARSACS were reported in more than 20 countries leading to a varied clinical spectrum of the disease (Dupre et al. 2006). Approximately 80% of ARSACS patients present the classic symptom triad of cerebellar, pyramidal, and neuropathic involvement before the age of 30 (Bouchard et al. 1998; Synofzik et al. 2013). First signs of the disease are often the result of pyramidal damage and generally first occur in childhood, appearing as imbalances, falls, deformities of the feet and hands, or spasticity in the lower limbs (Bouchard et al. 1998; Bereznyakova and Dupre 2018; Dupre et al. 2008). With time, symptoms become more present, and cerebellar damage will lead to speech, writing, or learning difficulties (Dupre et al. 2008; Duquette et al. 2013). During adolescence, ataxia-associated symptoms are more pronounced and lead to difficulties in the execution of fine movements or impaired coordination, which is explained by increased tendon reflexes (Dupre et  al. 2008; Duquette et  al. 2013). Neuropathic damages can also be detected by electromyography (EMG), which generally shows obvious signs of denervation and demyelination in distal muscles by the 20s (Bouhlal et  al. 2011). This disease feature was confirmed in nerve biopsies (Peyronnard et al. 1979; Takiyama 2006). Signs of distal amyotrophy can be severe, proprioceptive sensation in the lower limbs decreases, and Achilles reflexes decline or completely disappear with age (Bouchard et al. 1998; Bereznyakova and Dupre 2018). In addition, peripheral involvement usually increases after the age of 30. By

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the age of 40, affected patients often rely on a wheelchair (Bereznyakova and Dupre 2018). Magnetic resonance imaging (MRI) studies have revealed atrophy of the vermis, cerebellar atrophy, and loss of myelin in the corticospinal and posterior spinocerebellar tracts (Dupre et al. 2006, 2008; Bouhlal et al. 2011). Besides these progressive symptoms, other nonprogressive manifestations were observed, such as saccadic eye tracking or retinal nerve fiber hypermyelination (Bouchard et al. 1998; Garcia-Martin et  al. 2013). The average life expectancy of ARSACS patients is estimated to be in the range of 51–61  years old (Bouchard et  al. 1998; Garcia-­ Martin et al. 2013).

4.2 Pathophysiology The sacsin molecular chaperone (SACS) gene is mapped on the long arm of chromosome 13 (13q12.12) and contains a total of 10 exons. Among them, 9 exons are coding exons including a gigantic 12,794 pb exon. SACS encode the protein sacsin (Ouyang et al. 2006; Engert et al. 2000). Sacsin is a 4579 amino acid protein highly expressed in Purkinje cells, precerebellar nuclei, and corticospinal motor neurons (Ouyang et al. 2006; Engert et al. 2000; Parfitt et al. 2009). The protein is also found in fibroblasts, skeletal muscle, and, at lower levels, in the pancreas (Engert et al. 2000). Its role is not fully understood but it was shown to be involved in protein degradation, proper protein conformation (through heat shock protein homology domain), ATP hydrolysis, organization of the intermediate filaments network, and mitochondrial fission processes (Engert et al. 2000; Parfitt et al. 2009; Romano et al. 2013; Girard et al. 2012; Anderson et al. 2010). Causal mutations in the SACS gene were suggested to create a loss of function (Engert et al. 2000; Kozlov et al. 2011). In the Charlevoix and Saguenay-Lac-St-Jean regions, most cases display both or one of the two following mutations in the SACS gene: a deletion producing a frameshift mutation (94% of carrier chromosomes), and a nonsense mutation expected to introduce a premature stop codon (3%) (Engert et al. 2000). Studies using animal models, primary cell lines, and fibroblasts derived from ARSACS patients have shown that there is a significant decrease in the level of sacsin (Girard et al. 2012; Lariviere et al. 2019; Bradshaw et al. 2016). Pathogenic sacsin alterations lead to a disruption of mitochondrial homeostasis with hyperfused mitochondria so-called balloon-like accumulating in neuron cell bodies (Girard et al. 2012; Bradshaw et al. 2016) as well as increased mitophagy and formation of reactive oxygen species (ROS) (Morani et  al. 2019; Duncan et  al. 2017). It also leads to the formation of perinuclear neurofilament bundles (Lariviere et al. 2015, 2019; Duncan et al. 2017) as well as Purkinje cell degeneration (Girard et al. 2012; Lariviere et al. 2015, 2019).

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4.3 Diagnosis and Treatment Currently, diagnosis of the disease includes neurological examinations, MRI, nerve and motor conduction studies, and retinal examination (Bouhlal et  al. 2011). Molecular genetic testing is also available for establishing the ARSACS diagnosis (Vermeer et al. 2009). Moreover, a disease severity index has been established. The index includes eight tests that evaluate the lower and upper limbs as well as mobility, and was designed to consider cerebellar, pyramidal, and neuropathic disorders (Gagnon et al. 2019). While ARSACS remains an incurable disorder, patients can receive special cares, such as occupational, speech, and physical therapies. Patients are often treated with baclofen to control spasticity and anticholinergics may be given to relieve urinary problems (Bereznyakova and Dupre 2018).

4.4 Current Clinical Research Docosahexaenoic acid (DHA), known for its neuroprotective properties and for its ability to stimulate autophagy, has successfully been used in patients with spinocerebellar ataxia 38 (Manes et al. 2017, 2019). Prior investigations had been conducted in an FRDA mouse model (Abeti et al. 2015). Interestingly, regular intake of DHA for a 20-month period appeared to stabilize clinical symptoms in two siblings affected with ARSACS (Ricca et al. 2020). From a more fundamental perspective, treating dermal fibroblasts derived from ARSACS patients with Idebenone, an analog of coenzyme Q10 (CoQ10) and previously studied in FRDA, showed a significant decrease in ROS-positive cells (Martinelli et  al. 2020; Parkinson et  al. 2013). More recently, the same research group has focused on the benefits of Resveratrol in vitro, a known drug for its antioxidant properties and for its positive effects on neurodegenerative diseases such as Alzheimer’s disease (AD) (Loureiro et al. 2017). Another team also demonstrated that Resveratrol treatments contributed to a reduction in ROS levels in ARSACS-­ fibroblasts (Şen et al. 2021). Inhibition of Hsp90 is another track of investigation. Nethisinghe and collaborators have tested the therapeutic potential of Hsp90 inhibition with the molecule KU-32, a C-terminal-domain-targeted compound (Nethisinghe et al. 2021). The compound was found to reduce vimentin bundling in carrier and patient cells and to restore the mitochondrial electron transport chain. Recently, a proteomic study compared the cell lysates of fibroblasts isolated from ARSACS patients and SH-SY5Y neuroblastoma cell line invalidated in sacsin with healthy control cells. Data analysis revealed deregulation in neuroinflammation, synaptogenesis, and cell engulfment in the ARSACS and sacsin-deficient SH-SY5Y cell populations compared to the control, leading to other potential therapeutic targets (Morani et al. 2020).

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5 SYNE-1-Related Ataxia (ARCA1 – SCAR8) 5.1 Clinical Features This slowly progressive ataxia was first described in the province of Quebec in Canada (Dupre et  al. 2007). In 2007, Gros-Louis and colleagues described an unusual type of inherited cerebellar ataxia called autosomal recessive cerebellar ataxia type 1 (ARCA1) (MIM#610743). It was also called spinocerebellar ataxia autosomal recessive 8 (SCAR8) and more recently, SYNE-1-related ataxia in reference to the affected protein. The median age of onset in the French-Canadian SYNE1 region is 31 years old (most cases within 17–50 years old). The onset of the disease generally occurs in adolescence to early adulthood in other groups (median 17 years old; most cases within 6–42 years old) (Mademan et al. 2016). Cerebellar and extracerebellar symptoms are commonly found in patients (Mademan et  al. 2016). Dysarthria, cerebellar ataxia, or both phenotypes can occur simultaneously. Following the start of the conditions, individuals acquire dysmetria, brisk lower extremity tendon reflexes, and mild abnormalities in saccades and smooth pursuit. Extrapyramidal symptoms, retinopathy, cardiomyopathy, sensory abnormalities, or autonomic disturbances are not seen in ARCA-1 individuals (Beaudin et al. 1993). They may also display cognitive deficits without psychiatric comorbidities (Laforce Jr. et al. 2010; Valentina Castillo et al. 2021).

5.2 Pathophysiology Pathogenic mutations in the SYNE1 gene encoding the synaptic nuclear envelope protein 1 (SYNE-1), also known as nesprin 1, are the cause of this disease. Located on the short arm of chromosome 6 (6p5), SYNE1 is one of the biggest genes in the human genome. It is 147 exons wide and it is transcribed into a 27,652-bp mRNA, which translates in an 8797 amino acid protein (Zhang et al. 2002). Four truncating mutations were initially discovered to cause SYNE-1 ataxia as well as two splice site mutations and one deletion. Compound heterozygous mutations were also identified in the SYNE1 gene (Gros-Louis et al. 2007). The result of those seven loss-of-­ function mutations is expected to be protein truncation (Noreau et al. 2013; Duan et al. 2021). SYNE-1 is expressed in Purkinje cells, olivary bodies, and in myocites (Arias et al. 2022). One of its functions is to form a link between the actin cytoskeleton and the organelles (Baumann et al. 2017). Dysfunction of the protein contributes to disrupting signaling between neurons in the cerebellum. Affected individuals may also suffer cognitive impairment (Laforce Jr. et al. 2010).

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5.3 Diagnosis and Treatment Diagnosis is generally made through MRI findings, which show diffuse cerebellar atrophy without brainstem involvement after only a few years of evolution (Dupre et al. 2007) and using molecular genetic testing for the presence of SYNE-1 biallelic pathogenic variants (Beaudin et  al. 1993). In most cases, nerve conduction studies come back negative, but neuropathies can occasionally be detected (Synofzik et al. 2016). MRI scans may appear normal more but may also show acute or chronic neurogenic changes in people with clinical evidence of motor neuron dysfunction (Izumi et al. 2013). There is no curative treatment for this disease. Clinical management’s efforts aim at improving patients’ mobility while reducing the risk of side effects. A multidisciplinary team should personalize follow-up of patients (Beaudin et al. 1993).

5.4 Current Clinical Research To our knowledge, few therapeutics are in development for SYNE-1-related ataxia. One of them is the CAD-1883 drug, a small conductance calcium-sensitive potassium channel positive allosteric modulator. A phase 1 clinical study has been already done and a phase 2 trial has been planned to confirm the safety and tolerability but is currently on hold (NCT04301284). Despite a very limited therapeutic perspective, fundamental progress to understand the exact function of nesprin is being made in other tissues. In fact, nesprin is known to be pathologically involved in Emery-­ Dreifuss muscular dystrophy (EDMD) type 4, a disease caused by disruptions of the nesprin/lamin/emerin interactions in cardiac and skeletal muscle cells (Holt et al. 2019; Janin and Gache 2018; Madej-Pilarczyk 2018; Fanin et al. 2015).

6 Ataxia Telangiectasia (AT) 6.1 Clinical Features Also known as Louis-Bar syndrome, AT (MIM#208900) is a rare genetic form of early-onset autosomal recessive ataxia. The prevalence of AT is estimated to be 1:40,000–1:300,000, with ATM allele heterozygosity representing 1.4–2% of the general population (Amirifar et al. 2020). The clinical picture consists of a combination of neurological and systemic symptoms. In particular, AT is characterized by cerebellar atrophy with progressive ataxia, oculocutaneous telangiectasias, oculomotor apraxia, a higher incidence of malignancy (particularly lymphoid malignancy), radiosensitivity, immune deficiency, recurrent sinopulmonary infections, and high levels of alpha-fetoprotein (AFP) in serum (van Os et al. 2016). Generally,

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the disease first manifests during early childhood when the toddler is beginning to sit and walk (Rothblum-Oviatt et  al. 2016). Affected children are more likely to develop cancer, leukemia and lymphoma being the most frequent forms that were reported. Furthermore, growth retardation is frequently observed in patients who may also develop type 2 diabetes at puberty (van Os et al. 2016). The phenotypic spectrum of AT is large due to the important variability of clinical presentation. Patients with mild or atypical presentation tend to have an adult onset (Tiet et al. 2020). Patients usually have a poor prognostic with an estimated survival time of 25 years. Because of progressive respiratory failure and/or malignancies, AT is usually fatal in the second or third decade of life (Boder and Sedgwick 1958). A British meta-analysis concluded that heterozygous carriers have a shorter life expectancy and a higher risk of cancer, particularly breast cancer and probably digestive tract tumors (van Os et al. 2016).

6.2 Pathophysiology This disease is caused by a mutation in the ataxia telangiectasia mutated (ATM) gene. More than 500 different mutations have been reported to cause this disease (Becker-Catania and Gatti 2001). Splicing, nonsense, and frameshift mutations are the most common ATM mutations listed in AT patients (Perlman et al. 2012). The gene is located on the long arm of chromosome 11 (11q22-23), encoding for the ATM protein. It is a serine/threonine protein kinase mediating a variety of cellular functions (Boohaker and Xu 2014). Protein amount and kinase function are two primary factors that determine the age of onset, progression, and clinical symptoms of AT. Absence of kinase activity in both alleles results in more severe phenotypes (Levy and Lang 2018). The gene contains 66 exons spanning approximately 150 (Buzin et al. 2003). More particularly, ATM has a role in DNA double-strand break (DSB) repair by positively influencing the activity of the suppressor protein p53 after DNA damage, thus inhibiting cell proliferation. Malformations such as gonadal dysgenesis, which is seen in AT patients, can also be caused by an alteration of the cell cycle control mechanisms. ATM is also necessary for the synthesis of immunoglobulin and the survival of lymphoid (Riboldi et al. 2021).

6.3 Diagnosis and Treatment Diagnosis of AT can be challenging for clinicians due to the rarity of the disorder and international guidelines. A combination of clinical features, family history, neuroimaging, and laboratory findings is generally required. Interestingly, the early onset of ataxia within the first decade in the classical form and oculomotor apraxia is usually crucial to guide the diagnostic. Two scales were used by Jackson and colleagues to evaluate the neurological development of AT. The AT index (or Crawford

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score) collects data on clinical findings of AT but does not differentiate between hyperkinetic and hypokinetic movement disorders (Jackson et al. 2016). The second scale, the A-T Neurological Examination Scale Toolkit is more detailed. It rates more specific information on ataxia like ocular function, hyperkinetic and hypokinetic movement abnormalities, and corresponds well with the total A-T index (Crawford et al. 2000). Telangiectasias are present in almost all patients and, therefore, represent a critical criterion to make the correct diagnosis. However, telangiectasias cases are not always easy to recognize (Perlman et al. 2012). Also, there were cases of atypica (Rothblum-Oviatt et al. 2016). The concomitant presence of tumors may raise the suspicion of AT. Abnormalities that can be detected in laboratories include high and slowly increasing AFP levels after 2 years of age and low serum levels of IgA, IgG, and IgE subclasses. Lymphopenia can also be observed.

6.4 Current Clinical Research No curative nor disease-modifying therapy is available for AT. Among other avenues of investigation, glucocorticoids such as dexamethasone have been studied with some success initially (Quarantelli et al. 2013; Broccoletti et al. 2011; Chessa et al. 2014). It has been suggested that dexamethasone can increase the synthesis of an alternative ATM transcript that retains some of the full-length ATM functions. However, this hypothesis is still controversial (Pozzi et al. 2020). As the oral administration pathway presents a long-term risk for the patients, a method for encapsulating dexamethasone sodium phosphate into autologous erythrocytes has been proposed (Chessa et al. 2014). This strategy is currently used in an international, multicenter, randomized, prospective, double-blind, placebo-controlled, phase 3 study (NCT02770807). Having already proved its utility in an Atm−/− mouse model (Yang et al. 2021), the intake of nicotinamide ribonucleoside, precursor of NAD+, as a food supplement for 4 months in patients led to an improvement of the ataxia scores, which then decreased with the withdrawal of the treatment (Veenhuis et al. 2021). The nicotinamide ribonucleoside is currently in phase 2 clinical trial as a food supplement for 2 years in AT subjects (NCT04870866). A phase 1 clinical trial is also ongoing to study the long-term effect of M6620 as monotherapy or in combination with carboplatin or paclitaxel (NCT03309150). M6620, an inhibitor of ataxia telangiectasia and Rad3-related (ATR) kinase, has already been used as a monotherapy or in combination with carboplatin, and has shown to have good tolerability and promising effects in patients with advanced cancers (Yap et al. 2020). In a more fundamental perspective, recent drug discovery strategies have explored the use of induced pluripotent stem cells (iPSCs). Wolvetang and collaborators have investigated the generation of iPSCs from olfactory neurosphere derived of AT patients (Leeson et al. 2021). Such cell population can be used to generate bidimensional and tridimensional (3D) patient-specific neuronal in  vitro models enabling fundamental investigations to better understand the underlying neurodegeneration mechanism. Using stem cell-derived brain organoids, inhibition of the

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cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway was shown to ameliorate the premature senescence hallmarks of AT-iPSC-derived neurons by preventing neuronal loss and rescuing neuronal function (Aguado et al. 2021). Interestingly, aspirin and small molecule inhibitors can prevent the activation of the cGAS-STING pathway (Aguado et al. 2021). It may represent a promising potential therapeutic target for treating neuropathology in AT patients.

7 Ataxia with Oculomotor Apraxia Type 1 (AOA1) 7.1 Clinical Features Ataxia with oculomotor apraxia type 1 (MIM#208920; AOA1) is characterized by a slowly progressive cerebellar ataxia, axonal sensorimotor neuropathy, hypoalbuminemia, and hypercholesterolemia with early onset (at an age of 7  in average). Affected individuals then develop areflexia and loss of ambulation leading to quadriplegia from 7 to 10 years after the apparition of the first symptoms (Coutinho et al. 1993). It is followed by oculomotor apraxia (OMA) a few years after onset and progresses into external ophthalmoplegia (Coutinho et al. 1993). Moderate cognitive impairment appears to be a common characteristic of AOA1 (Le Ber et  al. 2003). Individuals with OMA cannot normally fix objects in front of them because of horizontal saccades of elevated latency. They also find it difficult to look away; they tend to generally turn their heads before to follow with their eyes. Individuals who have had their heads immobilized were found to be unable to move their eyes (Coutinho et  al. 1993). Blinking can also be exaggerated in these individuals. A wheelchair is often required, usually by ages 15–20 years (Coutinho et al. 1993). AOA1 shares similarities with AT. However, people with AOA1 do not present with extra-neurological features, have a later onset, and are less likely to suffer frequent infections.

7.2 Pathophysiology The APTX gene encodes for a protein called aprataxin, a member of the histidine triad superfamily, which plays a role in DNA repair (Meagher and Lightowlers 2014). APTX is located in the 9p13 locus and has 9 coding exons (van Minkelen et  al. 2015). Aprataxin can modify the broken ends where the DNA damage is located and contribute to the repair through nonhomologous end joining. This protein catalyzes the nucleophilic release of adenylate groups covalently bound to the 5′phosphate terminal region. The same terminal region can then be reused as substrates for DNA ligases (Rass et al. 2007). Because aprataxin is not fully functional for individuals with AOA1, patients have shown enhanced sensibility to a variety of

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agents causing single-strand breaks (Garcia-Diaz et al. 2015). To date, more than 20 different mutations in the APTX gene are associated with AOA1 (Amouri et  al. 2004; Criscuolo et al. 2005; Date et al. 2001; Jacquemont et al. 2003; Moreira et al. 2001a, b; Sekijima et  al. 2003; Shimazaki et  al. 2002; Tranchant et  al. 2003; Castellotti et al. 2011; Rana et al. 2013). A study showed lower levels of CoQ10 in fibroblasts derived from AOA1 patients due to reduced transcription and transduction of prenyl diphosphanate synthass subunit 1 (Garcia-Diaz et al. 2015). However, the factors causing CoQ10 insufficiency induced by APTX mutations are still unknown.

7.3 Diagnosis and Treatment The diagnosis is classically made with an association of clinical findings, which includes family history and genetic analysis (Coutinho et al. 1993). Cerebellar atrophy can be detected by MRI in affected individuals. In addition, the exclusion of AT can be made when oculomotor apraxia is present. Signs of axonal neuropathy can also be described by the EMG in 100% of patients (Coutinho et al. 1993). Some symptoms like hypoalbuminemia and hypercholesterolemia can be treated and prevented with a low-cholesterol diet and lipid-lowering treatment, but patients need regular follow-up with a physician (Coutinho et al. 1993).

7.4 Current Clinical Research To date, only one clinical trial has been undertaken for AOA1, which is based on CoQ10 deficiency in the muscles of AOA1 patients to assess the benefits of CoQ10 supplementation (NCT02333305) (Le Ber et al. 2007). No result has been reported yet. In recent years, more fundamental research on AOA1 has focused greatly on the physiological function of aprataxin. Aprataxin has been shown to be involved in the repair of DSBs particularly through its ability to remove damaged 3′ and 5′ ends (Ahel et al. 2006; Takahashi et al. 2007). In AOA1, aprataxin lacks this functionality and nonadenylated DNA ligase has been shown to be deficient, resulting in the accumulation of unrepaired DSBs (Takahashi et  al. 2007; Reynolds et  al. 2009). More recently, Kato and collaborators have shown for the first-time immunological abnormalities in AOA1 patients, such as leukopenia, decreased levels of CD4+ T-lymphocytes, CD8+ T-lymphocytes, and B-lymphocytes, suggesting a new aspect to consider for therapy (Kato et al. 2021). In addition, the disappearance of hyposignal on MRI due to iron deposition in the dentate nuclei has been established as a novel AOA-specific biomarker (Ronsin et al. 2019). Finally, iPSC lines have been established from patient’s fibroblasts (Ababneh et al. 2020), which might help to facilitate studies on this disease.

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8 Ataxia with Oculomotor Apraxia Type 2 (AOA2) 8.1 Clinical Features AOA1 and AOA2 share similar clinical findings. A few unique features help differentiate these two types of ataxias, such as serum levels of AFP. Like AOA1, AOA2 (MIM#606002) is a slowly progressive ataxia characterized by cerebellar atrophy, axonal sensorimotor peripheral neuropathy, and an elevated serum level of AFP (Choudry et al. 2018). Other symptoms include occasional oculomotor apraxia in approximately 56% of cases (Al Tassan et al. 2012), strabismus, dystonic postures of the hands, choreic movements, head or postural tremor, extensor plantar responses or sphincter disturbances, and mildly impaired cognitive functions (Le Ber et  al. 2004; Mariani et al. 2017). The age of onset varies, with people being affected anywhere between 2 and 30 years old (Mhanni et al. 2016). The disease duration was estimated to range between 2 and 53  years (Moreira and Koenig 1993). Patients with the disease were reported to live close to their 80s (Moreira and Koenig 1993).

8.2 Pathophysiology AOA2 is caused by a mutation in the SETX gene encoding for the senataxin protein, a large C-terminal DNA/RNA helicase of 2677 amino acids (Choudry et al. 2018). Over a hundred homozygous mutations were discovered to date, including nonsense, missense, and splice site variants, as well as small insertions and deletions (Bohlega et al. 2011). Senataxin is expressed in the brain as well as the spinal cord and muscle cells. Of particular interest, senataxin dominant mutations have been linked to a familial form of juvenile amyotrophic lateral sclerosis (Chen et al. 2004). In the last years, it has been shown that the gene involved in AOA2 was induced in an oxygen-dependent manner. Under hypoxic conditions and through the protein kinase R-like endoplasmic reticulum kinase branch of the unfolded protein response, senataxin protected cells from transcription-related DNA damage and apoptosis (Ramachandran et al. 2021). In addition, senataxin is also thought to be involved in autophagy, providing a potential therapeutic target. Indeed, invalidation of the senataxin resulted in an accumulation of ubiquitinated protein, a decrease in the removal of aggregated protein and mitochondrial defects (Richard et al. 2020). This protein is also involved in the DNA damage response. Senataxin was shown to help with the recruitment of Rad51, an essential protein involved in DNA repair (Cohen et  al. 2018). It is also thought to be involved in telomere stabilization as well as in cell survival, preventing the translocation of RNA/DNA hybrids that form because of DSBs (Cohen et al. 2018; De Amicis et al. 2011).

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8.3 Diagnosis and Treatment Testing the serum levels of AFP can bring supplemental information for the diagnosis, considering that they can be elevated for AOA2 patients as opposed to AOA1 patients (Dragasevic-Miskovic et  al. 2021). However, studies have shown that patients can still be affected by AOA2 without having elevated serum levels of AFP (Paucar et al. 2019). AT should also be considered when oculomotor apraxia and/or high AFP serum concentrations are present. Several AOA2 patients show increased serum creatine kinase concentration (Moreira and Koenig 1993). In addition, young ataxic patients for whom the FRDA and AT diagnostic have been excluded should be tested for the SETX gene (Choudry et  al. 2018; Mignarri et  al. 2015). EMG reveals signs of axonal neuropathy in 90–100% of AOA2 patients (Bohlega et al. 2011). There is no treatment for this disease, but physiotherapy and other occupational therapy can provide some level of relief for patients especially for disabilities caused by peripheral neuropathy. By the age of 30, a wheelchair is usually required. Educational assistance should be provided to compensate for reading and writing difficulties caused by OMA and sensorimotor peripheral neuropathy (Bohlega et al. 2011).

8.4 Current Clinical Research Most research efforts have focused on elucidating the role of senataxin and its mechanism of action. Report on therapeutic clinical trial or a potential therapeutic avenue is still needed. Thus, in a more fundamental perspective, a model for studying AOA2 has been developed that may help to make further progress by recreating some aspects of the disease as the SETX knockout mouse that did not show neurological disorders (Becherel et  al. 2013). Indeed, Becherel and colleagues have developed a novel in vitro model using neurons differentiated from patient-derived iPSCs that exhibit a cellular phenotype typical of AOA2 patients, creating a promising tool for studying AOA2 (Becherel et al. 2015).

9 Ataxia with Vitamin E Deficiency (AVED) 9.1 Clinical Features The first case of ataxia with vitamin E deficiency (AVED) (MIM#227460) was reported by Burck and collaborators in 1981 (Koenig 2003). AVED manifests in late childhood to early teens between the age of 5 and 15 years old (Schuelke 1993). The clinical features are similar to FRDA and include areflexia, loss of proprioception, head dystonia, dysarthria, cognitive decline, and retinitis pigmentosa, which are

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also found among other progressive ataxias (Schuelke 1993). Less frequently, glucose intolerance and cardiomyopathy are among the conditions that can present (Mariotti et al. 2004). Cerebellar ataxia is usually the first symptom to appear and gets worse through the years, often leading to a wheelchair-bound state (Hentati et al. 2012). Head dystonia is not always present but is observed in 28–73% of the affected patients (Benomar et al. 2002).

9.2 Pathophysiology AVED is caused by mutations in the alpha-tocopherol transfer protein (TTPA) gene located on the long arm of chromosome 8 (8q13). Recessive mutations in the TTPA gene, which include nonsense, missense, and splice-site mutations, as well as minor deletions and insertions, are responsible for the disease. The majority of those who are impacted have private mutations, which are rare genetic mutations usually found only in a single family or a small population (Hentati et  al. 2012). His101Gln (H101Q) mutation in the TTPA gene appears to be more prevalent than other known mutations (Hoshino et al. 1999). The concentration of vitamin E is then reduced because of the damaged TTPA. Individuals who are homozygous or compound heterozygous with a mutation have nearly total penetrance for AVED. For nonaffected individuals, vitamin E is normally absorbed by the intestine and then secreted into plasma in chylomicron form. During catabolism of chylomicrons, vitamin E is transferred to particles of circulating lipoproteins. The main function of TTPA is to transport RRR-α-tocopherol. This cytosolic liver protein can distinguish between the eighth dietary vitamin E isomers (α-, β-, γ-, δ-tocopherols and α-, β-, γ-, δ-tocotrienols) and preferentially binds RRR-α-tocopherol to very low-density lipoproteins (VLDLs), which are then discharged into the circulation to the tissues (Hentati et al. 2012). When it comes to intestinal absorption of vitamin E and its incorporation into VLDL, individuals with AVED have a normal function (Traber et al. 1990).

9.3 Diagnosis and Treatment There is no consensus concerning the diagnosis of AVED. However, it is commonly diagnosed when there is an FRDA-like phenotype with a vitamin E deficiency. Indeed, in the absence of intestinal malabsorption, a low blood vitamin E value is used to make the diagnosis (Palau and Espinos 2006). AVED patients usually have T; p.Cys104Phe) in the voltage-gated calcium channel gene CACNB4 located on chromosome 2q22–23 (González Sánchez et al. 2019). The B4 subunit is the most highly expressed β subunit in the cerebellum (Escayg et  al. 2000). The EA5 mutant did not appear to alter channel kinetics, but substitution of residues may disrupt the conformation and interaction with other proteins including the α1 subunit (Escayg et al. 2000). This in turn may affect trafficking or function in a yet to be determined way resulting in the phenotype seen in EA5 (Escayg et al. 2000).

4.5 ITPR1-Related Ataxias Inositol 1,4,5-trisphosphate (IP3) is an intracellular second messenger generated by cleavage of the membrane phospholipid, phosphatidylinositol 4,5-bisphosphate (PIP2) into IP3 and diacylglycerol by phospholipase C. IP3 receptors (IP3Rs) are a group of calcium channels located in the membrane of the endoplasmic reticulum (ER) that play a significant role in intracytoplasmic calcium concentration. Their primary function is to release calcium from the ER into the cytoplasm after binding IP3, thus influencing intracytoplasmic calcium concentration and modulating cellular activities such as axonal transport, level of excitation, synaptic transmission, division, development and apoptosis (Terry et al. 2020; Tada et al. 2016; Keehan et al. 2021). The IP3R1 subtype of IP3 receptors gene is encoded by the gene ITPR1. IP3R1 receptors have three functional domains, an IP3 binding domain in the N terminus, a coupling/regulatory domain centrally, and a C-terminal transmembrane spanning pore (Foskett et al. 2007) (Fig. 4). As described below it appears that genetic disruption of these distinct domains in the IP3R1 protein may result in distinct clinical presentations. The prevailing belief is that IP3 binding to the N terminus of the receptor triggers conformational changes that result in opening the channel pore located at the C terminus, but the precise mechanism of this gating is uncertain (Terry et al. 2020). Mutations have been identified in each of these domains and may interfere with various aspects of receptor function including ligand

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IP3-binding suppression

Internal coupling domain

IP3 Ca2+ IP3

IP3

Open Cytoplasm

Gatekeeper domain M4-M5 linker Transmembrane domain

M1 M2 M3 M4 M5

M6

ER lumen M5-M6 loop

C (conserved) V (varialbe) region region

Fig. 4  IP3R channel structure (Yamazaki and Mikoshiba 2009). There are three functional domains: an IP3 binding domain in the N terminus, a coupling/regulatory domain centrally, and a C-terminal transmembrane spanning pore (Foskett et al. 2007). IP3 binding to the N terminus of the receptor triggers conformational changes that result in opening the channel pore located at the C terminus, but the precise mechanism of this gating is uncertain (Terry et al. 2020)

binding, allosteric regulation, ion permeation, protein folding, stability and localization (Terry et al. 2020). However, many of the downstream effects of these mutations are not yet well understood or poorly characterized and the pathogenic mechanism remains to be elucidated in conditions associated with the ITPR1 gene. The IP3R1 receptor is abundant in cerebellar Purkinje cells, cortex, hippocampus, thalamus, caudate, and putamen (Yamada et al. 1994; Tada et al. 2016). One thought is that reduced levels or excessive activation of IP3R1 may cause disruption of intracellular calcium homeostasis, which in turn causes dysfunction of Purkinje cells and eventually degeneration as well (Tada et al. 2016). Pathogenic mutations in the ITPR1 gene can cause several ataxic conditions, including spinocerebellar ataxias 15/16 and 29 and Gillespie syndrome (Fig. 4). 4.5.1 Spinocerebellar Ataxia Type 15 (SCA15) SCA15 typically has onset during adulthood and is slowly progressive. Some individuals retain ambulatory ability multiple decades after onset (Storey 2014). The disease phenotype is variable and common symptoms may include gait ataxia, dysarthria,

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limb ataxia, intention and/or postural tremor, head tremor, nystagmus, truncal ataxia, and pyramidal signs (Tipton et al. 2017). Mild dysphagia, mild cognitive impairment, impaired vestibulo-ocular reflex gain, and movement induced oscillopsia have also been reported (Storey 2014). Age of onset typically is age 30–50 years, but ranges from 7 to 72 years, and initial symptoms may include gait ataxia and tremor (Storey 2014; Tada et al. 2016). Imaging may reveal atrophy of the rostral and dorsal vermis of the cerebellum with mild cerebellar hemispheric atrophy (Storey 2014). SCA15 is an autosomal dominantly inherited condition typically caused by deletions in the ITPR1 gene, but missense mutations have also been reported (Tipton et al. 2017; Tada et al. 2016). The deletions are of various sizes and include larger deletions encompassing portions or all of theITPR1gene in association with partial deletions in the adjacent SUMF1 gene as well (Storey 2014; Tipton et al. 2017). SUMF1 is associated with the autosomal recessive disease multiple sulfatase deficiency characterized by mental retardation, seizures, and leukodystrophy (Tada et al. 2016). While these individuals are at risk of developing SCA15, they are not at risk of developing the other condition unless the non-deleted homologue of SUMF1 also has a pathogenic variant (Storey 2014). Penetrance is unknown. Given that most pathogenic variants are caused by deletion, anticipation is not likely (Storey 2014). It is presumed that haploinsufficiency and loss of function is the pathogenic mechanism due to the fact that most cases are usually caused by deletion in the gene, but the exact mechanism remains to be elucidated (Storey 2014; Tada et al. 2016; Keehan et al. 2021). In the case of the missense mutation, one hypothesis is that such a change might reduce the level of IP3R1 protein, because studies have shown that the functional properties of the mutated protein are largely unaffected due to the mutation (Tada et al. 2016). 4.5.2 Spinocerebellar Ataxia Type 16 (SCA16) First described in 2001 in a Japanese family with individuals found to have nystagmus and truncal ataxia, the SCA16 locus was eventually reassigned and a point mutation was found to have overlap with one that was earlier found in SCA15 (Miyoshi et al. 2001; Tipton et al. 2017). Later, another study found that a heterozygous deletion limited to exons 1–48 of ITPR1 was responsible and indicated again that haploinsufficiency and loss of function were the likely pathological mechanism for what is essentially a genetically identical disorder (Iwaki et  al. 2008; Tipton et al. 2017). The suggestion was then to make SCA16 a “vacant SCA” and place this family under the umbrella of SCA15 (Gardner 2008; Tipton et al. 2017). 4.5.3 Spinocerebellar Ataxia Type 29 (SCA29) SCA29 is a congenital, non-progressive ataxia associated with infantile-onset, hypotonia, gross motor delay (head control, sitting upright, standing, fine motor, speech; delayed by months or years) and mild cognitive impairment (Zambonin

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et al. 2017). Impaired ocular fixation and global developmental delay can also be seen (Zambonin et al. 2017). In addition to gait ataxia, the most common cerebellar signs were dysmetria, dysarthria and intention tremor, which were present in over 75% of individuals, but nystagmus, abnormal saccades, oculomotor apraxia, and dysdiadochokinesia were also seen (Zambonin et al. 2017). In one study reviewing the natural course of the disease, improvements across multiple domains including ataxia, tone, and eventual attainment of developmental milestones, speech, coordination, and motor function have been reported, though it is not clear whether or not this was due to early intervention (Zambonin et al. 2017). Cognitive impairment can range from none to moderate impairment (Zambonin et al. 2017). Imaging can show cerebellar atrophy, including superior cerebellar hemispheres and vermis (Zambonin et al. 2017). SCA29 is autosomal dominantly inherited condition and is usually caused by a heterozygous missense mutation (Zambonin et al. 2017). Some of these missense mutations were thought to affect the coupling/regulatory domain of the ITPR1 gene product. This domain contains binding sites that act as competitive inhibitors for IP3 and include IRBIT (inositol triphosphate receptor binding protein) and CARP (carbonic anhydrase-related protein VIII), which help modulate IP3R1 activity (Zambonin et al. 2017; Tada et al. 2016). Therefore, these mutations were thought to result in dysregulation of IP3R1 rather than haploinsufficiency by reducing the binding affinities of the IRBIT and CARP proteins and relatively increasing the affinity of IP3 to IP3R1 leading to exaggeration of the pathway mediated by IP3R1 (Zambonin et al. 2017; Tada et al. 2016). This may imply a gain of function rather than loss (Casey et al. 2017). However, some mutations also affect the transmembrane region as well (Zambonin et al. 2017). If the pathogenic mechanism was loss of function in this case, then one may hypothesize that the mutation has a dominant-­ negative effect on the function of the IP3R1 complex (Tada et al. 2016). A more recent study had shown one particular mutation resulted in alteration of the binding domain with replacement of a positively charged arginine residue with a neutral tryptophan residue, thus decreasing affinity for the negatively charged IP3 molecule (Terry et al. 2020). 4.5.4 Gillespie Syndrome (GS) GS is a rare congenital disorder first described by Gillespie in 1965. One of the most striking and consistent features is bilateral partial aniridia resulting in a fixed and large pupil (Keehan et al. 2021). Iris hypoplasia results in scalloped or “festooned” edges at the pupillary border with iris strands extending onto the anterior lens surface at regular intervals (Hall et al. 2019). Other common characteristics include congenital hypotonia, nonprogressive ataxia (including gait and balance impairment, incoordination, intention tremor, and scanning speech), delay in meeting motor milestones and varying degrees of intellectual disability (Keehan et al. 2021; Gerber et al. 2016; McEntagart et al. 2016). Other reported features in patients identified as having GS include facial dysmorphism, cardiac defects including

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pulmonary valve stenosis and patent foramen ovale and gastrointestinal defects, including intestinal malrotation (Hall et al. 2019; Paganini et al. 2018; McEntagart et al. 2016; Carvalho et al. 2018). Imaging studies have shown cerebellar atrophy/ hypoplasia, including vermian and superior cerebellar atrophy (Stendel et al. 2019; McEntagart et al. 2016). The mode of inheritance for GS includes both autosomal recessive and dominant mutations in ITPR1. Causative variants include single-nucleotide missense mutations and deletions that cluster frequently near or within the C-terminal transmembrane channel domain of the gene thus affecting ion transport (Keehan et al. 2021; Paganini et  al. 2018; Hall et  al. 2019). As an example, codon Lys2569 deletion resulted in decreased calcium release activity in mutant transfected cells, but this has not been demonstrated in vivo (Hall et al. 2019; Gerber et al. 2016). Variants have also been identified in the central regulatory domain (Paganini et  al. 2018; Stendel et al. 2019). Dominant mutations, which can be inherited or de novo, are thought to result in a dominant-negative effect with the prevailing model suggesting that they compromise the homotetrameric structure of the channel and thus the normal functioning of the pore (Paganini et al. 2018; Dentici et al. 2017; Gerber et al. 2016; McEntagart et al. 2016; Keehan et al. 2021; Hall et al. 2019). Recessive mutations are homozygous or compound heterozygous and result in truncation due to generation of premature stop codons. This leads to complete or occasionally partial loss-of-function with possible persistence of small amounts of truncated protein (Hall et  al. 2019; Paganini et  al. 2018). The severity of the disease may then be modulated by the position of the mutations, presence and amount of wild-type protein and the ability of mutated proteins to be incorporated without effecting the overall function of the channel (Paganini et al. 2018). To the contrary, one study found that only wild-type homotetramers were able to contribute a significant amount of calcium release, and if tetramers form without bias leading to a normal distribution of mutant and wild-type subunits in the tetramer, the reduced fraction of fully wild-type homotetramers would result in attenuated calcium release (Terry et al. 2020).

4.6 TRPC3-Related Ataxia/Spinocerebellar Ataxia Type 41 (SCA41) The first and only confirmed case of what is thought to be SCA41 was described recently. The patient was a 40-year-old white male of European ancestry who presented with 2 years of progressive imbalance and ataxic gait (Fogel et al. 2015). He had an extensive and unremarkable evaluation for acquired causes of ataxia, dominantly inherited ataxias, and there was no obvious family history, but he was estranged from his father (Fogel et al. 2015). His MRI brain showed mild vermian atrophy (Fogel et al. 2015). Exome sequencing revealed a single variant of potential clinical significance, which was a heterozygous point mutation (p.Arg762His; 122824185G>A) of the TRPC3 gene located on chromosome 4 (Fogel et al. 2015).

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This position is highly conserved and the protein change was predicted to be damaging. His known family was unaffected and the variant was not found maternally, but they were unable to determine if it was inherited or de novo due to lack of paternal data and therefore could not directly confirm pathogenicity (Fogel et al. 2015). TRPC3 is a part of the transient receptor potential family, which is expressed in Purkinje cells of the cerebellum even early in development (Becker 2017; Fogel et al. 2015). The gene encodes a non-selective cation channel permeable to sodium and calcium linked to key signaling pathways and synaptic transmission in Purkinje cells including the one mediated by metabotropic glutamate receptor subtype 1 (mGluR1) (Becker 2017). Activation results in calcium influx, but permeability to sodium might also activate voltage-gated calcium channels through changes in membrane potential (Becker 2017). The p.Arg762His variant is located within a highly conserved region implicated in regulating channel gating and a mutation would likely have a significant effect on function (Becker 2017). Toxic gain-offunction is the suspected mechanism of pathological dysfunction in SCA41. Mutant p.Arg762His channels were expressed in similar numbers to the wild-type TRPC3 at the plasma membrane in mouse models, but significantly induced neuronal cell death (Becker 2017). One sign that implicated increased channel activity was significantly increased nuclear localization of the calcium-­sensitive transcription factor NFAT in models where there was overexpression of the TRPC3 mutant (Becker 2017). The absence of ataxia symptoms in individuals with heterozygous deletions and rare nonsense variants in the population further supports the theory of toxic gain-of-function (Becker 2017). In mouse models, Purkinje cell loss and impairment in dendritic arborization during cerebellar development result from loss of TRPC3 or from point mutations that cause gain of function (Becker 2017). Purkinje cell firing is markedly abnormal in these models characterized by a depolarization block of spiking and alteration of intrinsic firing frequency (Bushart and Shakkottai 2019).

5 Ataxia Related to Mutations in Sodium Channel Genes 5.1 Ataxia Related to Voltage-Gated Sodium Channels Sodium channels initiate action potentials in neurons and excitable cells (Catterall 2018; Wagnon et al. 2018; O’Brien and Meisler 2013) (Table 3). The channels consist of a large central pore-forming α subunit in complex with one or two auxiliary β subunits that may be involved in the trafficking of α subunits (Catterall 2018) (Fig. 5). α-Subunits consist of four homologous domains (I–IV), each containing six transmembrane segments (S1–S6) and a pore-forming loop. (Alexander et al. 2011) The positively charged fourth transmembrane segment (S4) acts as a voltage sensor and is involved in channel gating (Alexander et al. 2011). The S5 and S6 segments combine with the connecting pore-loops (P-loops) to form the channel pore

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Table 3  Ataxia related to mutations in sodium channel genes Ion channel name/ gene Nav1.1/ SCN1A

Ion channel type Voltage-­ gated

Mode of inheritance/ effect of Ataxia mutation type Dravet Autosomal syndrome dominant/loss-­ of-­function

NaV1.2/ Voltage-­ Episodic SCN2A gated ataxia

Nav1.6/ SCN8A

Voltage-­ gated

Additional distinguishing Age of features onset Infantile Drug-resistant onset epilepsy, developmental delay, moderate to severe intellectual disability, parkinsonism, high risk of early mortality from SUDEP Early onset Seizures early in Autosomal dominant/gain-­ (10 months life to 14 years) During ataxic of-­function episodes, encephalopathy, dystonic posturing, myoclonus, vomiting, hyperreflexia can be present Weekly to monthly episodes in most cases with episodes typically lasting minutes to hours May range from Autosomal developmental and dominant/gain-­ epileptic of-­function or encephalopathy loss-of-function (DEE; most common presentation) to benign epilepsy Intellectual disability, autism, ataxia, myoclonus, choreoathetosis, paroxysmal dyskinesia may coexist or occur independent of seizures

Cerebellar atrophy present or not May be present

May be present

May be present

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Fig. 5  Voltage-gated sodium channel structure (Denomme et al. 2019). Channels consist of a large central pore-forming α subunit in complex with one or two auxiliary β subunits that may be involved in the trafficking of α subunits (Catterall 2018). α-Subunits consist of homologous domains I–IV, each containing transmembrane segments S1–S6 and a pore-forming loop (Alexander et al. 2011) The positively charged S4 segment acts as a voltage sensor and is involved in channel gating (Alexander et al. 2011). The S5 and S6 segments combine with the connecting pore-loops (P-loops) to form the channel pore and ion-selectivity filter (Reynolds et  al. 2020). Connecting loops link the transmembrane segments, larger intracellular loops link the four homologous domains, and the intracellular loop between domains III and IV is the fast-inactivation gate (Reynolds et al. 2020). Membrane depolarization facilitates a conformational change in the channel which leads to channel activation, movement of the positively charged S4 voltage-segment domain and opening of the ion selectivity pore, allowing sodium influx (Reynolds et al. 2020). During depolarization, the channel inactivates during the fast inactivation phase by the inactivation gate folding into and occluding the channel pore (Reynolds et al. 2020)

and ion-selectivity filter (Reynolds et  al. 2020). Connecting loops link the transmembrane segments, larger intracellular loops link the four homologous domains, and the intracellular loop between domains III and IV is the fast-inactivation gate (Reynolds et  al. 2020). Membrane depolarization facilitates a conformational change in the channel which leads to channel activation, movement of the positively charged S4 voltage-segment domain and opening of the ion selectivity pore, allowing sodium influx (Reynolds et al. 2020). During depolarization, the channel inactivates during the fast inactivation phase by the inactivation gate folding into and occluding the channel pore (Reynolds et al. 2020). Hyperpolarization releases the inactivation gate and enables channel activation once again (Reynolds et al. 2020). Sodium channels rapidly activate, open and inactivate in response to depolarizing stimuli and an additional slow inactivation process is engaged by long trains of stimuli or prolonged depolarization (Catterall 2018).

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5.2 SCN1A-Related Ataxia/Dravet Syndrome Dravet Syndrome (DS) is a rare infantile onset, chronic epileptic encephalopathy characterized by drug-resistant epilepsy, developmental delay, and high risk of early mortality (17% by 20 years of age) mainly due to sudden unexpected death in epilepsy (SUDEP) or status epilepticus (SE). DS may represent 2–3% of children with refractory epilepsy who transition to adult care (Andrade et  al. 2021). Fever or hyperthermia may trigger the initial seizure, which typically occurs before age 19 months (Andrade et al. 2021). Seizure types are variable and can include GTCs, myoclonic, absence, focal impaired seizures and others. Seizures can be prolonged, cluster or result in SE (Andrade et al. 2021). Moderate to severe intellectual disability is common and individuals can regress and lose acquired skills following a prolonged seizure or episode of SE (Andrade et al. 2021). Autism can also co-occur with DS (Andrade et al. 2021). Behavioral abnormalities include attention deficit disorder, agitation, irritability, and aggressiveness (Andrade et al. 2021). Patients who are ambulatory may develop a wide-based ataxic gait with other features developing as they age including crouching, camptocormia, dystonic posturing, tiptoeing, and parkinsonian quality (Andrade et al. 2021). Cerebellar speech patterns and other features of parkinsonism, including cogwheeling rigidity and bradykinesia, along with anterocollis may also be present. Parkinsonism can be levodopa responsive (Andrade et al. 2021). 80–90% of patients with a clinical diagnosis of DS have a pathogenic variant in the SCN1A gene which encodes the α1 subunit of the neuronal voltage-gated sodium channel Nav1.1 expressed in GABAergic interneurons (Andrade et  al. 2021; Catterall 2018; Gataullina and Dulac 2017). DS is an autosomal dominant disorder primarily caused by heterozygous, de novo loss-of-function mutations involving Nav1.1, but germline mosaicism or somatic mosaicism have also been reported (Catterall 2018; Andrade et al. 2021). Half the variants have mutations leading to truncation and reduced protein expression and the others have missense mutations in the pore forming part or the voltage sensor part leading to haploinsufficiency (Scheffer and Nabbout 2019; Gataullina and Dulac 2017). The loss of function of these sodium channels may explain why sodium channel blocking anti-epileptic drugs exacerbate seizures in DS. Variants may alter Nav1.1 activation as well as the slow inactivation of interneurons (Layer et al. 2021). Mutant channels may open and reach their maximal activation at more depolarized potentials (Layer et  al. 2021). Entry into slow inactivation was accelerated and voltage dependence was shifted to more hyperpolarized potentials in one study (Layer et al. 2021). The loss-­ of-­function mutations had the specific effect of reducing the sodium currents and electrical excitability of GABAergic interneurons, which decreases inhibitory input and leads to overall hyperexcitability throughout the brain at baseline predisposing to seizures (Catterall 2018). On the other hand, ataxia is thought to occur due to failure of action potential firing in GABAergic Purkinje neurons (Catterall 2018).

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5.3 SCN2A-Related Ataxia SCN2A mutations have been associated with a wide spectrum of clinical presentations including benign familial neonatal seizures (BFNIS), developmental and epileptic encephalopathy (DEE), autism-spectrum disorders, intellectual disability and rarely, episodic ataxia (EA) (Passi and Mohammad 2021). One group reviewed the 21 cases associated with EA in the literature that had been reported up to that point (Schwarz et al. 2019). EA onset ranged from 10 months to 14 years, but was usually early onset (Schwarz et  al. 2019). The frequency of EA episodes is weekly to monthly in most cases with episodes typically lasting minutes to hours, but ranged from brief, daily events to 1–2 episodes per year each lasting several weeks (Schwarz et al. 2019). Reported ataxic features included dysarthria, poor balance, hypotonia, and additional features during events included encephalopathy, dystonic posturing, myoclonus, vomiting, hyperreflexia, and tremor (Schwarz et  al. 2019; Passi and Mohammad 2021; Liao et al. 2010a). Triggers included stress, sleep deprivation, head trauma, and various sensory stimuli. The large majority of these patients also had concurrent seizures that had onset usually within the first 3 months of life, often during the neonatal stage and on the milder side (Schwarz et al. 2019). Cognitive outcomes were mostly favorable with most patients having normal or mild impairment in most cases with rare exceptions (Schwarz et al. 2019). Cerebellar atrophy was reported in a few individuals, but it was not a consistent feature. SCN2A-associated EA is often due to de novo mutations, but familial cases have also been found (Schwarz et al. 2019). They are most often the result of heterozygous missense mutations and autosomal dominant (Passi and Mohammad 2021; Schwarz et al. 2019). SCN2A encodes the alpha subunit of the voltage-gated sodium channel Nav1.2, which is highly expressed in unmyelinated parallel fibers, which are the axons of granule cells in the molecular layer that project to Purkinje neurons, and the axon initial segments and nodes of Ranvier of myelinated nerve fibers of hippocampal and cortical excitatory neurons (Liao et al. 2010a; Wolff et al. 2019). The prevalence of these channels may vary during different periods of development in the different neuronal types, which may explain why seizures have earlier onset than EA in affected patients and even remit in some cases (Liao et  al. 2010a; Schwarz et al. 2019). Nav1.2 is still highly expressed in the dendrites of adult hippocampal and cortical neurons, but, as patients age, it may be replaced by Nav1.6 in the region of the axon initial segment (Liao et al. 2010b). While a number of mutations have been found in association with SCN2A-associated EA, there appear to be two mutational hotspots. One common mutational hotspot resulted in p.Ala263Val missense mutation affecting the S5 segment of the domain I in the Nav1.2 alpha subunit (Schwarz et al. 2019). Another hotspot for pathogenic variants is the S4 segment and its cytoplasmic loop within domain IV (Schwarz et al. 2019). This would affect gating of the channel as well as inactivation. The various mutations associated with EA are all thought to lead to a gain-of-function, which may lead to increased sodium current in a variety of ways. These include a hyperpolarizing shift, meaning channels open at voltages closer to resting membrane potential and faster activation,

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a depolarizing shift with more channels available for activation at resting membrane potential, slower inactivation, or accelerated recovery after fast inactivation with shortened refractory period after an action potential (Hedrich et  al. 2019). Such changes would then lead to an increased persistent sodium current with downstream effects of membrane depolarization in neurons, amplification of synaptic potentials, generation of subthreshold oscillations, and facilitation of repetitive firing maintaining prolonged depolarized plateau potentials (Hedrich et  al. 2019). This state of hyperexcitability would understandably lead to susceptibility to seizures. Given this knowledge, sodium channel blockers, such as phenytoin, are the anti-convulsants of choice and are indeed beneficial for the treatment of seizures (Schwarz et al. 2019). However, these drugs were less effective and often not helpful for EA for unclear reasons (Schwarz et al. 2019). Acetazolamide, which has been of benefit for forms of EA, had benefit in a few of the patients in improving frequency and severity (Schwarz et al. 2019). Interestingly, loss-of-function variants in the SCN2A gene, which are associated with autism spectrum disorder and intellectual disability, also result in epilepsy in 20–30% of affected children (Spratt et al. 2021; Zhang et al. 2021b). While sodium channel loss in excitatory cells would be expected to have the opposite effect, pyramidal neurons in mice lacking Nav1.2 channels were found to be hyperexcitable (Spratt et al. 2021; Zhang et al. 2021b). Downregulation or dysfunction of potassium channels in affected individuals prevented proper repolarization between action potentials allowing neurons to reach the threshold for action potential generation more rapidly, increasing susceptibility to seizures (Spratt et al. 2021; Zhang et al. 2021b).

5.4 SCN8A-Related Ataxia Ataxia has also been associated with mutations of the SCN8A gene, but it may not be the sole, initial or primary manifestation. The phenotype and severity of presentation of SCN8A-related disorders is variable and is rapidly increasing in recognition with more widespread testing and whole exome sequencing. Mutations in SCN8A have been implicated conditions ranging from developmental and epileptic encephalopathy (DEE) to benign epilepsy. Neurological features such as intellectual disability, autism, and movement disorders, including ataxia, myoclonus, choreoathetosis, and paroxysmal dyskinesia, may coexist or occur independent of seizures in SCN8A-related disorders (Gardella and Møller 2019; Larsen et al. 2015). DEE is the most common phenotypic presentation and is characterized by early onset seizures that are often drug-resistant (43 days to 4 months), severe to profound intellectual disability, absent speech, progressive pyramidal and extrapyramidal signs (myoclonus, dystonia, dyskinesia), axial hypotonia and tetraparesis with progressive cerebral atrophy, and cortical blindness (Gardella and Møller 2019). Less frequently SCN8A mutations are implicated in benign familial infantile seizures associated with paroxysmal kinesigenic dyskinesia (PKD) (Gardella and Møller

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2019). In this presentation, seizures are typically self-limiting and occur during the first year of life and 33% developed PKD in teens (Gardella and Møller 2019). In between these two phenotypes exists an intermediate form characterized by a milder epilepsy phenotype than DEE, normal cognition to moderate intellectual disability, and mild or absent neurological deficits (Gardella and Møller 2019). The mean age of epilepsy onset was 14 months and a majority of patients achieved seizure freedom by age 4–10  years on pharmacological agents (Gardella and Møller 2019). ADHD and autistic traits were observed in some and varying degrees of gait disturbances, ataxia, tremor/myoclonus, hypotonia, movement disorders, and sleep disorders were also reported (Gardella and Møller 2019). A small number of patients have SCN8A variants without epilepsy and phenotypic presentations have included mild to moderate intellectual disability with comorbid behavioral symptoms (autism, ADHD, etc.) and discrete neurological symptoms including ataxia, gait instability, hypotonia, speech delay, dyskinesia, tremor, chorea, and myoclonus (Gardella and Møller 2019). The true prevalence of this subtype may be unknown since SCN8A mutations may have gone undetected for these sporadic cases (Gardella and Møller 2019). A 2006 case report commented on a 9-year-old boy with intellectual delay, ADHD, motor delay, ataxia, and pancerebellar atrophy with individuals in his family sharing the same mutation and various other manifestations including cognitive impairment and ADHD (Trudeau et al. 2006). SCN8A-related conditions are autosomal dominant disorders. The gene, located on chromosome 12q13, encodes the α subunit of Nav1.6, which is a voltage-gated sodium channel located in the initial segment of the axon involved in the initiation and propagation of action potentials as well as in the nodes of Ranvierin-myelinated neurons (Gardella and Møller 2019; Wagnon et al. 2018). Nav1.6 is also thought to be important in the generation of the persistent and resurgent currents necessary for rapidly firing neurons such as Purkinje cells (O’Brien and Meisler 2013). Nav1.6 is widely expressed in the brain and is highly expressed in Purkinje cells (Gardella and Møller 2019; Wagnon et al. 2018). SCN pathogenic variants are typically missense mutations in the highly conserved transmembrane domain and tend to be de novo heterozygous mutations, but rarely are inherited due to mosaicism in an unaffected parent (Gardella and Møller 2019). Mutations in the inactivation gait and the cytoplasmic C terminal domain have also been found (Gardella and Møller 2019; Meisler 2019). Gain-of-function mutations have been associated with epilepsy syndromes and are characterized by elevation in sodium currents due to hyperactivity of Nav1.6 as a result of altered voltage dependence leading to premature channel opening or closing, delayed channel inactivation, or increased resurgent or persistent current that leads to increased neuronal firing and excitation (Gardella and Møller 2019; Meisler 2019; Larsen et al. 2015). The properties of these mutations may explain why sodium channel blockers may have more positive outcomes than other types of anti-epileptic drugs with the exception of drug-­ resistant DEE (Gardella and Møller 2019; Larsen et  al. 2015). Loss-of-function mutations are more commonly associated with intellectual disability, autism, and movement disorders, such as ataxia and myoclonus, without seizures (Gardella and Møller 2019). These include protein truncation mutations (C terminal domain in

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ataxic patient above), shifted voltage dependence of activation, delayed channel inactivation, impaired trafficking to initial segment of axon, and complete or partial loss of channel activity resulting in reduced neuronal excitability in mouse models (Meisler 2019; Larsen et al. 2015; Trudeau et al. 2006; Wagnon et al. 2018; O’Brien and Meisler 2013). Genotype-phenotype correlation is not straightforward and often results in a spectrum of disorders even with the same mutation (Larsen et al. 2015).

6 Ataxia Related to Mutations Genes Encoding Na+/ K+ ATPase 6.1 ATP1A3-Related Ataxia/CAPOS Syndrome CAPOS syndrome is classically characterized by early onset cerebellar ataxia with a relapsing course, areflexia, pes cavus, optic atrophy, and sensorineural hearing loss (Salles et al. 2021). The initial episode follows a febrile illness and usually has onset between 1 and 5 years of age (Salles et al. 2021). Symptoms include acute onset cerebellar ataxia, encephalopathic features, hypotonia, areflexia and weakness (Salles et  al. 2021). Other less common symptoms include paresis and transient impairment of hearing and vision (Salles et al. 2021). Most patients recover completely after this initial event, but some have residual ataxic symptoms (Salles et al. 2021). Patients may then have two to three more episodes with a degree of recovery before transitioning to a slowly progressive chronic condition (Salles et al. 2021). In addition to ataxia, patients develop sensorineural hearing loss that may seem acute in onset and progressive, bilateral optic atrophy due to optic neuropathy that leads to loss of vision, poor color discrimination, and diminished brightness sensitivity, nystagmus and strabismus (Salles et al. 2021). Less common symptoms associated with CAPOS include urinary urgency, cardiac arrhythmia, left ventricular enlargement, scoliosis, cognitive dysfunction, autistic traits, bradykinesia, myoclonus, chorea, tremor, oral dyskinesias, and dystonia (Salles et al. 2021). While areflexia is agreed upon as a characteristic of CAPOS, pes cavus is a more controversial symptom as one group remarked that the prevalence was only slightly higher than the general population (Heimer et  al. 2015). There are also two related conditions attributed to mutations in ATP1A3, rapid-onset dystonia parkinsonism (RDP) and alternating hemiplegia of childhood (AHC). These are now thought to exist on a spectrum of disorders with CAPOS, with both clinical overlap unique manifestations, such as adult-onset relapsing encephalopathy with cerebellar ataxia, adult-­ onset cerebellar ataxia, fever-induced paroxysmal weakness and encephalopathy, paroxysmal non-kinesigenic dyskinesia (Salles and Fernandez 2020; Salles et al. 2021). CAPOS syndrome is inherited in an autosomal dominant fashion and is due to a heterozygous missense mutation, p.Glu818Lys, in the ATP1A3 gene on

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chromosome 19q (Demos et al. 2014). The ATP1A3 gene encodes the α3 isoform, which is one of four α isoforms (α1–4) in the heterotrimeric α-β-γ protein complex that constitutes the Na+/K+ ATPase (Salles and Fernandez 2020; Salles et al. 2021). The Na+/K+ ATPase is a transmembrane ion-pump that extrudes three sodium ions in exchange for two potassium ions into the cell for every adenosine triphosphate utilized. The pump helps to maintain and regulate the electrochemical gradient and is critical for action potential propagation during depolarization (Salles et al. 2021). Thus, even though it is not an ion channel per se, its function is critical to creating the gradient required for other ion channels to function properly. The α3 isoform is mainly expressed in neurons, especially in the basal ganglia, substantia nigra, red nucleus, thalamus, cerebellum, oculomotor nucleus, reticulo-tegmental nucleus of the pons, hippocampus, retina, spiral ganglion, and organ of Corti (Salles and Fernandez 2020). The α3 isoform acts as a rescue pump after repeated action potentials for rapid restoration of large transient increases in intracellular sodium ion concentration (Salles and Fernandez 2020). It may also support reuptake of neurotransmitters (Salles et al. 2021). The p.Glu818Lys mutation is thought to reduce the sodium ion affinity of both the internal and external cation-binding sites (external more so than internal) (Roenn et al. 2019). The reduced sodium ion affinity at the internal sites leads to delayed clearing of the accumulated sodium after an action potential (Roenn et al. 2019). The mutant Na+/K+ ATPase possesses a weaker voltage dependence and stronger potassium ion inhibition, which reduces the inability to rapidly regain the resting membrane potential following action potentials (Roenn et al. 2019). While this model may suggest a loss-of-function mechanism, an alternative theory points to a temperature-sensitive gain-of-function mechanism (Salles et al. 2021).

7 Other Ion Channel Disorders 7.1 SLC1A3-Related Ataxia/Episodic Ataxia Type 6 (EA6) EA6 due to mutations in the gene, SLC1A3, is a rare cause of episodic ataxia with only a few families described up to this point. In addition to paroxysmal and intermittent cerebellar dysfunction, individuals may also experience seizures and migrainelike headaches (Chivukula et al. 2020). Physical and emotional stress, heat, caffeine, alcohol, febrile illness and smoking were some factors seen to trigger events (Choi et  al. 2017b). Patients who have EA6 tend to have longer lasting attacks (several hours to days) and lack other features found in other EAs such as myokymia and tinnitus (Chivukula et al. 2020). Patient phenotype and severity may vary depending on mutation. The most severe phenotype is associated with the heterozygous missense mutation p.Pro290Arg (p.P290R) mutation was characterized by early onset severe episodic and progressive ataxia, cerebellar atrophy, seizures, alternating hemiplegia, and migraine (Jen et al. 2005; Chivukula et al. 2020). Interictal symptoms included

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hyperreflexia, saccadic pursuits, impaired optokinetic nystagmus, and truncal ataxia (Jen et al. 2005). p.Met128Arg (p.M128R) was associated with onset at 11 months with episodic truncal ataxia, dysarthria, tremor, and strabismus (Iwama et al. 2018). Acetazolamide was effective for her symptoms (Iwama et al. 2018). Another family of 3 patients with the p.Cys186Ser (p.C186S) mutation presented with episodes of ataxia with nausea, photophobia, vertigo, slurred speech, and diplopia/blurred vision from early childhood and interictal gaze evoked nystagmus that was acetazolamideresponsive, but one family member who carried the mutation remained unaffected (De Vries et  al. 2009; Chivukula et  al. 2020). Another family with the mutation, p.Val393Ile (p.V393I), had documented episodes of unsteadiness and dizziness with late onset (age 55) with eventual development of truncal ataxia and slurred speech with interictal symptoms including nystagmus and abnormal saccades (Choi et al. 2017b). This family had two affected and two unaffected family members with the mutation and affected individuals were acetazolamide responsive (Choi et al. 2017b). The pThr318Ala (p.T318A) mutation was found in patients with ataxia, dizziness and dysarthria. (Chivukula et al. 2020). The last described pathogenic missense variant in this gene, p.Arg454Gln (p.R499Q), was discovered during exome sequencing of undiagnosed inherited ataxias and was characterized by upper limb ataxia, dysarthria, abnormal saccades, and dysphagia that was continuous and progressive (Pyle et al. 2015; Chivukula et al. 2020). EA6 has autosomal dominant inheritance with both familial and de novo cases recognized. There is also suggestion of incomplete penetrance as there were unaffected carriers in certain families as noted above. The gene affected is the SLC1A3 which encodes the glial excitatory amino acid transporter 1 (EAAT1) that helps to clear glutamate from the synaptic cleft and regulates its concentration at excitatory synapses in the cerebellum, brainstem and thalamus, while also functioning as an anion channel (Chivukula et al. 2020; Iwama et al. 2018; Winter et al. 2012). EAAT1 contain eight α-helical transmembrane domains (TMD) and re-entrant hairpin loops (HP) 1 and 2 flanking TMD7 (Choi et al. 2017b). The first six TMDs form a scaffold that surrounds a C-terminal core domain comprising HP1, TMD7, HP2 and TMD8 (Choi et al. 2017b). The C-terminal domain is known to play an important role in transporting glutamate by inducing conformational rearrangements (Choi et  al. 2017b). Among them, TMD7 is critical binding site for glutamate as well sodium, hydrogen and potassium ions (Choi et al. 2017b). Furthermore, several residues in TMD7 contribute to anion permeation and selectivity (Choi et al. 2017b). EAATs are trimeric proteins with three subunits associating via immobile trimerization domains (Chivukula et al. 2020). Each subunit contains a mobile transport domain with substrate-binding sites that shuttle substrates in both directions and they function independently (Chivukula et al. 2020). This may suggest that a dominant negative effect resulting from the combination of wild type and mutant proteins may be less likely the pathogenic mechanism but could result in impaired trafficking due to retention of these heterotrimers (Chivukula et al. 2020). The p.P290R mutation is located within the trimerization domain, but all other known mutations are located in the transport domain (Chivukula et  al. 2020). EAATs act as secondary-­active glutamate transporters and anion channels and EAAT1 specifically transports

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glutamate stoichiometrically coupled to three sodium ions and one hydrogen ion in exchange for one potassium ion (Chivukula et al. 2020). The anion channel portion opens upon lateral movement of the transport domain from intermediate translocation states, tightly linking anion currents to transitions within the glutamate uptake cycle (Chivukula et al. 2020; Choi et al. 2017b). In other words, glutamate movement may gate the anion channel and anion currents may limit glutamate release (Choi et al. 2017a). As noted above, the mutations lead to variable phenotypes with differences in severity and associated features. The p.P290R mutation, which seems to be the most severe and best characterized, was shown to impair glutamate transport and form gain-of-function of anion channels by increasing probability of opening of the channel (Chivukula et  al. 2020). A mouse model demonstrated that excessive chloride ion efflux led to glial apoptosis and cerebellar atrophy early in development (Chivukula et al. 2020). The hypothesis was that such changes would result in an increase in the force driving GABA transporters for reuptake of GABA and then reduce inhibitory synaptic transmission in EA6 patients (Chivukula et al. 2020; Winter et al. 2012). Degeneration of glia would ultimately impair and reduce glutamate reuptake leading to increased glutamate-driven excitation, modify synaptic transmission in the cerebellum and lead to cerebellar degeneration in these animals (Chivukula et al. 2020; Winter et al. 2012). One group studied the potential mechanisms of the other mutations using heterologous expression in mammalian HEK293T cells. The p.M128R variant predicted a 50% reduction in EAAT1 glutamate transport and anion current suggestive of a loss of function of homotrimeric transporters and possible dominant negative effect in heterotrimeric ones. As noted, this model may be controversial given that these subunits are thought to function independently with impaired trafficking the more likely mechanism (Chivukula et al. 2020). On the other hand, the p.T318A mutation caused increased glutamate uptake and anion current amplitude twofold due to increased expression of the mutant protein (Chivukula et al. 2020). The other mutations were thought to have less drastic effects and mechanisms may include impairment of the cotransport process of glutamate due to abnormality in binding sodium ions (p.V393I), mild or even no reduction in glutamate uptake and anion currents, and increased expression of mutants with impairment of late steps in membrane surface insertion of EAAT1leading to it being in close proximity to the membrane instead of being properly inserted, which would reduce macroscopic glutamate uptake and anion currents (Chivukula et al. 2020).

7.2 Ataxia Related to Mutations in ANO10/Autosomal Recessive Cerebellar Ataxia Type 3 (ARCA3) ARCA3, also known as spinocerebellar ataxia recessive type 10 (SCAR10), is a rare recessively inherited ataxia. The disease is characterized by slowly progressive spastic ataxia (limb ataxia, dysarthria, nystagmus, saccadic abnormalities, ataxic

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gait) variably associated with motor neuron involvement and pyramidal signs (hyperreflexia), epilepsy, and cognitive decline (Nanetti et  al. 2019). The age of onset is usually in late teens and early adulthood (Nanetti et al. 2019; Yang et al. 2020). Cerebellar atrophy is seen on MRI (Nanetti et al. 2019). Bradykinesia/parkinsonism, mild vertical gaze paresis, pes cavus, and sphincteric disturbances have also been reported (Nanetti et al. 2019). Additional features may include cognitive decline, reduced levels of coenzyme Q10, and elevated serum alpha-fetoprotein (Nanetti et al. 2019). Cognitive impairment is not thought to be common. Executive function impairment can be seen even in those with reportedly normal cognition otherwise (Nanetti et al. 2019). ARCA3 is the result of mutations involving the gene, ANO10, located on chromosome 3p21.33. This gene encodes an eight transmembrane protein named anoctamin 10, which is a putative member of a family of calcium-activated chloride channels (Nanetti et al. 2019). ANO10 expression is mostly found in the adult brain and is especially high in the cerebellum, frontal, and occipital cortices (Vermeer et al. 2010). Individuals may carry either homozygous or compound heterozygous mutations that include missense, nonsense/truncation and deletion (Nanetti et  al. 2019). While missense mutations are most common, the c.132dupA mutation is regarded to be the most common found in heterozygosity and leads to a frame shift, introducing a premature stop codon (Nieto et al. 2019). It is known that calciumactivated chlorine channels have important functions including regulation of neuronal excitability (Vermeer et al. 2010). However, the exact function of anoctamin 10 and pathogenesis of mutations remains unclear. It could well be that the ANO10 gene product, in addition to the function of calcium-dependent chloride channel, could also influence calcium signaling in Purkinje cells, and a dysfunctional or absent anoctamin 10 may cause cerebellar ataxia via this mechanism (Vermeer et al. 2010). Anoctamin 10 may have a role in regulation of compartmentalized calcium signaling including release of calcium from intracellular stores (Benarroch 2017). Calcium signaling is important in proper function of Purkinje cells and abnormal signaling has been shown to be a pathophysiological mechanism in autosomal dominant ataxias (Benarroch 2017). Perhaps, ANO10 mutations lead to Purkinje cell dysfunction, calcium triggered neurodegeneration or both (Benarroch 2017).

8 Conclusion Cerebellar ataxia due to ion channel dysfunction or disruption is an evolving topic as much of what we know regarding expression or function of the channels is still being investigated or verified. Combined with this improved knowledge and improved methods of identification and recognition with innovations such as whole exome sequencing, we may be able to use this information to tailor treatment to better ameliorate the effects of the loss or gain-of-function mutations affecting these channels.

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Part II

Biomarkers and Tools of Trials

How to Design a Therapeutic Trial in SCAs Caterina Mariotti, Mario Fichera, and Lorenzo Nanetti

Abstract  Spinocerebellar ataxias (SCAs) are rare autosomal dominant inherited neurological disorders characterized by progressive cerebellar symptoms. In the past decades, several pharmacological and non-pharmacological symptomatic treatments were tested in clinical trials for their efficacy towards ataxia, but no long-­ lasting effective therapies have been yet established. In this chapter we briefly reviewed the literature on both pharmacological trials and rehabilitating treatments performed in SCAs, with the aim of gathering information on trial objectives and methodology and discuss fundamental elements to consider in future trials. For the design of meaningful clinical trials, the research question and associated hypotheses need to be well understood in terms of characteristics of the disease, intervention under study, target population, and measurement instruments. Randomized placebo-controlled designs are considered the primary research methodology for control of biases and confounding variables. New adaptive trial designs are also providing interesting options in order to reduce the number of subjects and speed up therapeutic deployment in rare diseases. In fact, the most challenging factors in clinical trials for SCA diseases are to maximize trial power with the minimum number of subjects, and to rely on the most sensitive outcome measures. Large collaborative initiatives on natural history studies for SCAs will provide the perfect support to ensure recruitment of a correctly powered number of patients, the number of appropriate sample sizes with targeted selection of stratified patient groups, and the knowledge about responsiveness to changes of the currently available outcome measures.

C. Mariotti (*) · M. Fichera · L. Nanetti Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy e-mail: [email protected]; [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_8

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At present, the most important gap to be filled with respect to trial readiness in SCAs is the definition of outcome measures that will efficiently capture disease-­ related functional and structural changes occurring during disease course or in response to therapeutical interventions. The rarity and additional clinical diversity of conditions may require differential outcome sets for different purposes and different phases of the diseases (e.g., pre-clinical phase, and symptomatic stages). The other necessary and complementary element to advance in cure of SCAs will be availability of innovative and efficacious therapeutic options allowing real improvements in patient’s life. Keywords  Randomized controlled trials (RCT) · Outcome measure · Trial design · Clinical interventions · Trial phases

1 Introduction Study designs are commonly classified in two main categories: studies in which the subjects are merely observed and their characteristics recorded for analysis, called observational studies, and studies involving an active intervention, such as a drug, a procedure, or a treatment, called experimental studies (Dawson-Saunders and Trapp 2004). Observational studies include cases-series, case control, cross-sectional, and cohort studies. Cross-sectional studies analyze data of a group or groups of subjects collected at one time. Case-control and cohort studies involve an extended observational period and, thus, are longitudinal studies. Longitudinal observational studies are extremely important because they provide valuable data on the reliability and responsiveness to change of the utilized outcome measures, such as effect size, standardized response mean, and signal-to-noise ratio. These indexes allow an estimate of the “true” change, related to disease progression, compared to random variability of repeated assessments. Further, such datasets yield important information on the interrelation of outcome assessment at different levels which can, for example, help to estimate the clinical relevance of changes observed. Clinical trials are experimental studies involving humans, and their purpose is to draw conclusions about a particular procedure or treatment. There are two major categories of clinical trials: those with and those without controls. Controlled trials are studies in which the experimental drug or procedure is compared with a group treated with another drug or procedure, a placebo, or a previously accepted treatment. The two most common designs are (a) non-randomized controlled trials (non-RCTs), and (b) randomized controlled trials (RCTs). In nonrandomized controlled trials, a concurrent comparison group is part of the study, and patients are allocated to this group by a nonrandom process. Data from such studies are usually considered reliable only if confirmed by a randomized study or by a meta-analysis of a number of similarly designed nonrandomized studies.

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In RCTs, individuals are randomly allocated to two or more treatment groups, which usually include a standard treatment group and one or more experimental groups (Stanley 2007). To reduce the chances that both treated subjects and the rater investigators may introduce a bias in the evaluations by favoring what they are expecting or hoping to see, the trial may be designed as a double-blind trial. In this case neither the subjects nor the investigators are aware whether the subject is assigned to experimental treatment or to control arm. Control may be a placebo, a sham procedure or the treatment or procedure commonly used, called standard of care or reference standard. Clinical trials are also classified by study objective and phases, and the conventional model of progressing from phase I to phase III is considered the standard paradigm for drug development. Phase I trials evaluate safety of a new drug or intervention (usually involving 10–30 subjects), phase II trials assess efficacy (20–50 patients), and phase III trials confirm safety and efficacy in a much larger group of subjects (usually 100–1000 patients). Phase III RCTs usually are and should be powered to confirm hypothesized effect of treatment, usually informed by effects of observed in phase II. According to their purposes, phase III trials are also called “confirmatory trials.” More recently, adaptive trial design has been proposed as a mean to increase the efficiency of RCT. An adaptive design is defined by the US Food and Drug Administration (FDA) as “a clinical trial design that allows for prospectively planned modifications to one or more aspects of the design based on accumulating data from subjects in the trial” (FDA 2019). The European Medicine Agency (EMA) defines a study design as adaptive “if the statistical methodology allows the modification of a design element (for example, sample-size, randomization ratio, number of treatment arms) at an interim analysis with full control of the type I error” (EMA 2007). Control for type I error in clinical trial represents the probability of identify a treatment effect, when in real the treatment has no effect (false positive). Adaptive designs are applicable to both exploratory and confirmatory clinical trials. Adaptive designs for exploratory clinical trials deal mainly with finding safe and effective doses or with dose–response modeling (Bhatt and Mehta 2016). These trials allocate patients to multiple different treatment doses, and patient responses are assessed at interim analyses. The purpose of the adaptive design is to be able to allocate more patients to the treatment doses of interest, while reducing allocation of patients to non-informative doses or doses eliciting safety concerns. In general, adaptive design allows a modification in randomization ratio, treatment arms, sample size estimation, and trial hypothesis in response to interim analyses results (Bothwell et al. 2018). Adaptive designs for confirmatory trials have been distinguished in different categories, such as, for example, seamless phase II–III designs, and sample-size re-­ estimation designs. Seamless phase II–III design reduces the time lag between phase II and III and allows continuing the trial without stopping patient enrolment and, more importantly, advance at least part of the study population seamlessly into the next phase of the study. The two main advantages of adaptive seamless design are the reduction of numbers needed until confirmation of efficacy is reached and the possibility to

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adapt design to observations if there remains uncertainty at study start, for example, on features of the population under study (Bhatt and Mehta 2016; Bothwell et al. 2018). The final step, after successful confirmatory phase III studies, is to monitor the safety of an approved intervention in a “real world” scenario, which is the purpose of phase IV studies.

2 Lessons from Clinical Trials Performed in SCAs Spinocerebellar ataxias (SCAs) are rare, clinically, and genetic heterogeneous disorders, transmitted with an autosomal dominant mode of inheritance. In the past decades, several interventional clinical trials have been performed in SCA patients (Salman 2018; Zesiewicz et al. 2018); however, no long-lasting symptomatic therapies or disease-modifying therapies have been identified so far. Several pharmacological clinical trials were designed to test a number of compounds not specifically developed for SCA diseases, but previously approved for human administration with various medical indications. Compounds include antioxidant agents, neuroprotective factors, antiepileptic medications, or supplement (Yap et al. 2021). The majority of these compounds were proposed after encouraging observation of symptomatic effects reported in a limited number of subjects; however, none of the pharmacological approach investigated so far were confirmed as efficacious therapies for SCAs. Although the ideal goal will be a disease modifying effect, able to slow, or halt disease progression, the identification of compounds with a significant and long-lasting symptomatic effect can also serve as means of modifying clinical course. This is for example the case of the treatment with acetazolamide or 4-aminopyridine in dominant episodic ataxia type 2 (EA2). In these patients, in fact, the reduction in frequency and severity of the ataxia attacks can greatly alleviate clinical dysfunction and substantially modify patient experience. In the following sections, we will briefly review the literature on clinical trials for both pharmacological and rehabilitating treatments performed in SCAs, with the aim of gathering information about trial methodologies and obtaining fundamental elements to consider in future trials.

2.1 Pharmacological Interventions We briefly reviewed the literature on pharmacological interventional trials in SCAs performed during the last two decades to collect and analyze overall information about trial design, trial objectives, study duration, number and characteristics of enrolled subjects, and primary endpoint of the studies. We analyzed 25 studies (from January 2001 to February 2022) (Table  1). All studies can be categorized as phase II trials, except the study reported by Nishizawa

Subjects N. Drug/ Placebo 13/0

10/0

15/0

11/0

39/0

13/0

6/0

13/0

14/0

15

Drug Acetyl-DL-leucine

Acetyl-DL-leucine

3,4-diaminopyridine

Gabapentin

Tandospirone

Fluoxetine

Acetazolamide

IGF-1

Trehalose

D-cycloserine

OL multiple dose Single-blind, placebo lead-in phase

OL

OL

OL

OL

OL

Study design OL case series OL case series OL

1 1

2

1

1

1

1

SCA3, SCA6, NGAsa

SCA3

SCA3, SCA7

SCA6

SCA1, SCA2, SCA3, SCA6, NGAsa SCA3

Center N. Ataxia type 1 SCA1, SCA2, NGAsa 1 SCA2, SCA6, SCA8, NGAsa 1 SCA6 and 16q-ADCA 1 SCA6

26

104

88

6

4

4

1

1

Duration (weeks) 1

NESSCA SARA ICARS

SARA

EDSS UPDRS ICARS

ICARS

ICARS

ICARS

SARA

Outcome measure SARA

No changes in ataxia scores −1.8

0.0 −2.0 ICARS score significantly reduced +0.6

−3.2

−3.1

−0.6

−0.6

Mean change End of study Drug −3.3

Table 1  Comparison of study design, duration, and patient characteristics in recent pharmacological interventional trials in SCAs

−0.6

Mean change End of study Placebo

(continued)

Arpa et al. (2011) Zaltzman et al. (2020) Ogawa et al. (2003)

Monte et al. (2003) Yabe et al. (2001)

Reference Strupp et al. (2013) Pelz et al. (2015) Tsunemi et al. (2010) Nakamura et al. (2009) Takei et al. (2010)

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20

22

24/12

23/23

9/9

20/20

28/27

22/23

Buspirone

Trimethoprim-­ sulfamethoxazole

Valproic acid

Ondansetron

Varenicline

Riluzole

Riluzole

Riluzole

Subjects N. Drug/ Placebo Drug Branched-chain amino 16 acid

Table 1 (continued)

26

RCT

RCT

RCT

RCT

52

52

8

8

RCT 12 multiple doses RCT 1

RCT Crossover

Study design RCT Crossover multiple doses RCT 12 Crossover

Duration (weeks) 4

8

3

1

2

4

2

1

1

SCA1, SCA2, SCA28, FRDA, FXTAS, NGAsa SCA1, SCA2, SCA6, SCA8, SCA10, FA SCA2

SCA3

SCAs, FA, NGAsa

SCA3

SCA1, SCA2, SCA3, SCA6, FA, SCA17, DRPLA, NGAsa SCA3

Center N. Ataxia type 1 SCA6, SCA7, NGAsa

−8.0 −0.9 +0.2

+1.7

−5.1 −1.9 −7.1 −1.0

ICARS

SARA

SARA

SARA

ICARS

+0.3

−0.8

−2.6b

+0.5

−0.6

−0.1

Ataxia rating scale SARA

+3.4

Mean change End of study Drug −4.3b +1.6

ICARS

Outcome measure ICARS

Mean change End of study Placebo −1.4

Coarelli et al. (2022)

Romano et al. (2015)

Lei et al. (2016) Bier et al. (2003) Zesiewicz et al. (2012) Ristori et al. (2010)

Schulte et al. (2001)

Assadi et al. (2007)

Reference Mori et al. (2002)

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RCT

18/18

31/31

9/8

140/138c

Zinc sulphate

Lithium

Lithium

Rovatirelinc 28

48

48

26

Duration (weeks) 16

86

1

1

1

SCA6, SCA3, NGAsa

SCA2

SCA3

SCA2

Center N. Ataxia type 1 SCA38

SARA

NESSCA SARA SARA

Outcome measure SARA ICARS SARA

−1.6

−1.0

Reference Manes et al. (2017) Velázquez-­ Pérez et al. (2011a) Saute et al. (2014) Saccà et al. (2015) Nishizawa (2020)

Pharmacological interventional trials in SCAs performed between 2001 and 2022. Study characteristics (intervention, design, duration, included patients and number of centers involved) as well as main results are reported. Mean change refers to the mean difference in clinical scores between baseline and end-of-­ treatment. Negative figures indicate improvement in total clinical score OL open-label, RCT Randomized Controlled Trial, ICARS International Cooperative Ataxia Rating Scale, EDSS Expanded Disability Status Score, UPDRS Unified Parkinson Disease Rating Scale, SARA Scale for the Assessment and Rating of Ataxia, NESSCA Neurological Examination Scale for Spinocerebellar Ataxias, FA Friedreich ataxia, IGF insuline growth Factor, NGAsa non-genetic ataxias, wk weeks, n.a. not available, bchange for the most effective dosage, c pooled data from two studies

RCT

RCT

RCT

Study design RCT

Subjects N. Drug/ Placebo Drug Docosahexaenoic acid 5/5

Mean change End of Mean change study End of study Placebo Drug −3.0 −0.3 −5.0 +0.4 SARA scores lowered without differences between treatment arms −0.35 compared to placebo −0.96 compared to placebo +0.3 +0.8

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et  al. (2020). In this phase III study, the authors reported a pooled retrospective analysis performed by combining the data from two phase II trials on rovatirelin. Thirteen studies recruited different populations of subjects including patients with different types of hereditary ataxia (SCAs and Friedreich ataxia) and patients with non-genetic ataxia, such as multisystem atrophy-cerebellar type. We further identified 12 phase II studies with recruitment confined to single SCA genotypes: 6 studies recruited SCA3 patients (Schulte et  al. 2001; Monte et  al. 2003; Zesiewicz et al. 2012; Saute et al. 2014; Lei et al. 2016), 3 studies recruited SCA2 patients (Velázquez-Pérez et  al. 2011a; Saccà et  al. 2015; Coarelli et  al. 2022), 2 studies SCA6 patients (Yabe et  al. 2001; Nakamura et  al. 2009), and 1 study was performed on SCA38 patients only (Manes et al. 2017). An open-label design (OL) was adopted in 9 out of 25 studies (36%), one study was a single-blind study with a placebo lead-in phase, and 15 were randomized, placebo-controlled studies (60%). In OL trials, the mean number of enrolled subjects was 15 patients (median: 13, range: 6–39), the studies were all conducted in a single center, and the mean duration of the treatments was 26  weeks (median: 4 weeks, range: 1–88 weeks). The 15 RCT studies had a mean of 50 enrolled patients (median: 36, range: 10–140), 7 were multi-center studies and 8 were single-center studies, and the mean duration of treatments was 24 weeks (median: 21, range: 1–52 weeks). Two studies had safety and tolerability as the main endpoint, and clinical effect of the treatments was considered secondary endpoint (Zaltzman et al. 2020; Saccà et al. 2015). In 18 out of 25, studies, endpoint and sample size calculation were not specifically provided. Only in five trials the primary endpoint was clearly stated, and it was represented by the evaluation of symptom improvement in treated patients versus no improvement/deterioration in placebo group (Saute et al. 2014; Romano et al. 2015; Nishizawa et al. 2020; Coarelli et al. 2022). The clinical outcome measures utilized in the trials were mainly the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS) (Schmitz-Hübsch et al. 2006; Trouillas et al. 1997). At the end of the period of drug administration, clinical rating scores decreased in 20/25 studies (suggesting less severe ataxia signs), and increased in 5 studies. Differences between treatment and placebo arms were statistically significant in 5 out of 13 RCT studies, with a mean decrease of 2.2 points in SARA score, and 5.5 points in ICARS score (Mori et al. 2002; Ristori et al. 2010; Romano et al. 2015; Lei et al. 2016; Manes et al. 2017). Patients treated with rovatirelin showed a statistically significant improvement in SARA score, in comparison with placebo, only when pooled analyses of two trials were performed (Nishizawa et al. 2020). It is also worth to mention that in several RCTs (usually with less than 28-week duration) a decrease in clinical scores was also frequently reported in the placebo treated subjects (−8.0 to −0.6 points in ICARS; −1.0 to −0.3 in SARA), thus demonstrating the extent of the placebo effect (Table 1). Three compounds were tested in more than one study: acetyl-DL-leucine was tested in two OL trials including ataxic patients with different genetic and non-­ genetic diagnoses; lithium was tested in SCA3 and in SCA2 patients in two different

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RCTs, and riluzole was tested at the same dosage in three different RCTs (Table 1). The first riluzole trial showed an improvement in ICARS scores after 8 weeks of treatment in 20 subjects with acquired and genetic ataxias (Ristori et al. 2010); the second trial showed improvement of SARA scores after 1 year of treatment in 28 subjects with different hereditary ataxias (Romano et al. 2015); and the third trial showed no effect of a one-year treatment in 22 patients with SCA2 (Coarelli et al. 2022). The different results in the three riluzole trials, based on the same dose regimen, most likely depend on differences on trial designs including patient number, trial duration, outcome measures, monocentric versus multi-center setting, and, above all, patient selection. In sum, the presentation of all previous evidence may serve as a reference of difficulties and errors encountered in symptomatic interventions. The design of such studies may not be an appropriate template for upcoming trials that are most likely aiming to delay progression or even manifestation.

2.2 Rehabilitation Interventions Physiotherapy is often suggested to SCA patients to improve gait, balance, and coordination. In the past years, several studies have assessed the impact of physical therapy on ataxia severity with positive results. Type of rehabilitating treatments varies between studies and includes conventional coordination and balance training, multidisciplinary inpatient rehabilitation, cycling, treadmill, occupational therapy, and computer-assisted training. Similarly, the timing and intensity of the intervention display high variability across studies. As an example of clinical studies on ataxia rehabilitation for ataxia symptoms, we selected six RCTs providing Class I and Class II evidence according to a recent systematic review (Yap et al. 2021), and an OL study for its longer follow-up period of 1 year (Ilg et al. 2010). In RCTs, the control groups received no intervention (Rodríguez-Díaz et  al. 2018; Tercero-Pérez et al. 2019; Miyai et al. 2012; Bunn et al. 2015), conventional physical therapy (Wang et al. 2018), or health education advices with exercises of upper limbs (Chang et al. 2015). Interventions were of different types, such as in-patient neurorehabilitation combining physical and occupational therapy (Miyai et al. 2012), intensive conventional physiotherapy (Rodríguez-Díaz et  al. 2018; Tercero-Pérez et  al. 2019; Ilg et  al. 2010), exergames-serious videogames (Wang et  al. 2018), balance training with optokinetic stimuli (Bunn et al. 2015), and cycling (Chang et al. 2015). Intensity of the intervention ranged from 15 minutes to 6 hours per day during weekdays; duration of the intervention regimen ranged between 4 and 24 weeks (mean: 9 weeks, median: 4 weeks). In four studies, the trial enrolled a homogenous population of patients with the same genotype (Rodríguez-Díaz et al. 2018;Tercero-Pérez et al. 2019; Wang et al. 2018; Bunn et al. 2015), while in three studies a mixed population of patients with ataxia of different etiology was considered. Mean number of treated

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Fig. 1  Rehabilitation trials in SCA. The graph shows the mean changes in SARA score, from baseline to the end of rehabilitation period, in six recent interventional trials in SCA patients. Patients were treated with different types of rehabilitation and physical therapy. Decrease in SARA scores represent improvement in ataxia tasks. Solid line indicates treatment period, dashed line indicates follow-up period. SARA Scale for the Assessment and Rating of Ataxia

patients was 21 (median: 16 patients, range: 9–38). SARA scale was adopted as outcome measure for ataxia severity in all cases except one that used ICARS (Chang et al. 2015). As displayed in Fig. 1, a significant improvement following rehabilitation was reported in the all studies, with a mean decrease in SARA score of 2.3 points. Data on long term follow-up after the intervention period were reported only in two studies (Wang et  al. 2018; Ilg et  al. 2010). The clinical assessments performed 24–52  weeks after the end of physiotherapy showed a progressive loss of the acquired benefit, suggesting that physiotherapy in SCA patients may be associated with a consistent but temporary effect on ataxic symptomatology.

3 Fundamental Aspects to Consider in SCA Clinical Trial Information generated by trials is useful when the trial has clear hypotheses and research questions, and when the choice of study procedures and trial design are adequate to answer these questions. The principal aspects that emerged from the revision of selected clinical trial in SCAs, both pharmacological and non-pharmacological, were the great variability in number of recruited subjects, trial duration, selection of SCA population, and choice

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of outcome measures. These aspects greatly impact on results of the studies, and are fundamental aspects to be taken into consideration when planning clinical trials.

3.1 Participant Number and Trial Duration The number of subjects is one of the greatest challenges to clinical trial due to the rarity of these disorders (Brooker et  al. 2021). The global prevalence of SCAs assessed in population-based studies ranged from 0 to 5.6 cases per 100,000 individuals, with an average of 2.7 cases per 100,000 individuals (Ruano et al. 2014). The relative frequency of the different SCA subtypes shows marked geographical and ethnic variability, often owing to founder effects. Currently, polyglutamine SCAs (SCA1, SCA2, SCA3/MJD, SCA6, SCA7, SCA17, and DRPLA) are the most commonly recognized genetic forms of SCAs. As a matter of fact, polyglutamine SCAs are the easiest forms to be detected by diagnostic tests available in all ataxia centers, mainly because these forms share a common type of genetic mutation, located in a specific region of each causative gene. SCA3/MJD is the most common SCA worldwide (20–50% of families), followed by SCA2 (13–18%) and SCA6 (13–15%) (Klockgether et al. 2019). Other SCA subtypes caused by untranslated repeats or conventional mutations are rarer than polyQ SCAs, and their precise distribution and frequency may be underrated due to requirement of advanced molecular techniques for diagnosis. All clinical trials for SCAs have the limiting factor of recruiting a sufficient number of patients, particularly in monocentric studies. The recruitment of a too small sample size could lead to inconclusive results or misleading results due to a selection bias. This is the case, for example, when a small group of enrolled patients may not be representative of the cohort of patients in the real-world setting. There are at least two complementary approaches to increase trial power and therefore reduce the number of required subjects. One approach is to select the most appropriated and best performing outcome measures. The other approach is to choose a specific target group of patients, where lower variability and faster progression of the disease is expected. For both these aspects, a valuable contribution can derive from the existing collaborative efforts for natural history studies in SCAs such as the Integrated Project on Spinocerebellar Ataxia (EuroSCA) and Spinocerebellar Ataxia Type 3/Machado-Joseph Disease Initiative (ESMI) in Europe, and the Clinical Research Consortium for Spinocerebellar Ataxias (CRC-­ SCA) in the United States (Schmitz-Hubsch et  al. 2010; Ashizawa et  al. 2013; Jacobi et al. 2015; Lin et al. 2020). In fact, Prospective cohort studies in SCAs provide fundamental information on clinical features, disease course, and on responsiveness to changes for specific SCA genotypes (Table 2). In addition, large collaborative research network can facilitate multicenter conduct of RCTs in SCA by faster recruitment of sufficient sample sizes. It is necessary that the number of patients will be based on precise calculation of sample size that depends on primary hypothesis but is also closely linked to

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Table 2  Mean annual change in natural history for the most common SCA genotypes Cohort of patients N. of Geographic site centers 17 EUROSCA Multicenter European study

Observation period (months) 24

17 EUROSCA Multicenter European study

49 (median)

SARA

CRC-SCA Multicenter USA study

12

Up to 24

SARA

Multicenter Study (France)

7

36

SARA

Japan intractable diseases research Single Center (Taiwan)

8

36

SARA

1

Up to 38

SARA

Multicenter (Brazil)

2

12

SARA NESSCA

Outcome measure SARA

Subjects number per genotype 117 – SCA1 163 – SCA2 139 – SCA3 107 – SCA1 146 – SCA2 122 – SCA3 87 – SCA6 39 – SCA1 52 – SCA2 93 – SCA3 54 – SCA6 25 – SCA1 35 – SCA2 58 – SCA3 5 – SCA6 10 – SCA7 46 – SCA6

Annual change in ataxia scale scores 2.18 (0.17)a 1.40 (0.11)a 1.61 (0.12)a

11 – SCA2 45 – SCA3 9 – SCA6 5 – SCA17 38 – SCA2

Reference Jacobi et al. (2011)

2.11 (0.12)a 1.49 (0.07)a 1.56 (0.08)a 0.80 (0.09)a

Jacobi et al. (2015)

1.61 (0.41)a 0.71 (0.31)a 0.65 (0.24)a 0.87 (0.28)a 1.8 (0.3)a 1.3 (0.2)a 1.7 (0.2)a 0.4 (0.4)a 1.6 (0.4)a 1.33 ± 1.40b

Ashizawa et al. (2013)

2.88 ± 2.32b 3.00 ± 1.52b 2.04 ± 0.76b 4.50 ± 2.22b 0.35–2.45c

Lee et al. (2011)

(SARA)

Tezenas du Montcel et al. (2012)

Yasui et al. (2014)

Monte et al. (2018)

1.03–2.14c (NESSCA)

Single Center(Brazil) Single Center (Brazil) Single Center (Taiwan)

1

13

ICARS

34 – SCA3 5.1

1

60

1

60

NESSCA 105 – SCA3 SARA 10 – SCA1 37 – SCA2 118 – SCA3 25 – SCA6 9 – SCA17

1.26 1.23 1.52 1.60 0.99 3.26

França et al. (2009) Jardim et al. (2010) Lin et al. (2019)

(continued)

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Table 2 (continued) Cohort of patients N. of Geographic site centers Single Center 1 (Cuba)

Observation period (months) 60

Outcome measure SARA

Subjects number per genotype 30 – SCA2

Annual change in ataxia scale scores 1.44

Reference Rodríguez-­ Labrada et al. (2016)

For annual change of clinical scale scores, data are expressed as: amean and standard error in parenthesis; bmean annual change ± standard deviation; cmean annual change in patients with 10 year disease duration ICARS International Cooperative Ataxia Rating Scale, SARA Scale for the Assessment and Rating of Ataxia, NESSCA Neurological Examination Scale for Spinocerebellar Ataxias

characteristics of the outcome chosen. Based on SARA score, the sample size estimation for a two-arm interventional study aiming at 50% reduction of progression and 80% power would require approximately 100 patients per group for a one-year trial (Jacobi et al. 2015). Highly reliable and sensitive outcome measures may allow shortening study duration and lowering its cost (Savelieff and Feldman 2021). Trial duration needs to be carefully programmed. A short study duration is generally associated with a more pronounce placebo effect both in the treated patients and in placebo groups. The placebo effect in several SCA studies has been demonstrated to be more pronounced in trials lasting less than 24–28 weeks (Table 1). SCAs are slowly progressive neurodegenerative disorders, and a short period of observation may fail to detect a real pharmacological effect associated with a decrease or halting in the progression rate. On the other hand, a too long study duration may be very expensive, and may imply that a patient population remains in a trial for prolonged period, also preventing the possibility of participating in other studies. This may result in competing priorities for different trials in which the same small number of subjects can participate. When designing a clinical trial, the treatment duration has to be considered in order to be able to assess effective changes in either clinical severity and /or disease progression. The chances of capturing significant real changes greatly depend on (i) the effectiveness of study drug, (ii) sensitivity of outcome measures, and (iii) possibility of controlling for confounding variables. A great number of natural history descriptions in different polyQ SCAs were able to demonstrate significant worsening over a one-year time period using different clinical rating scales. Indeed, the observation time (and sample size) needed for a trial is closely linked to reliability of the outcome chosen. Minimum requirement is that investigators can confidently expect a signal of progression/treatment above the “noise” of measurement, including the placebo effect. Thus, to determine appropriate trial duration for a study, investigators should search for evidence on outcome reliability and responsiveness, that is, expected progression rates over time from previous studies, definitions of smallest detectable change for the measure, and variability of change. It could be hypothesized that disease-modifying therapies may

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Fig. 2  Fundamental aspects to consider in SCA clinical trials. Scheme summarizing the critical aspects to consider in the design of clinical trials in spinocerebellar diseases

take longer to demonstrate a difference in disease progression compared to symptomatic therapies. In fact, a slowing or halting the natural course of SCAs may need several years to be ascertained, as minimal or no improvement in symptoms could be expected over time. Considering the existing ataxia scales, and in particular of SARA scale, a 12-month observational period would be minimum time to detect an effect on progression either due to a sustained symptomatic improvement or to a true disease-­ modifying impact of the intervention (Fig. 2).

3.2 Selection of SCA Population In selecting a population to enroll into a trial, researchers must consider the target use of the intervention because this will impact the possibility to generalize the results of the trial to the target population (Evans 2010). In trials testing symptomatic therapies different populations of ataxic patients were enrolled (Table 1) (Yap et al. 2021). Quite often both autosomal dominant and autosomal recessive forms were included in the same study, and, in a few trials, a mixed population of patients with genetic and non-genetic ataxias were also randomized in the same RCT (Romano et al. 2015). The enrollment of patients with different type of rare diseases may be advantageous to reach a larger number of subjects in a reduced period of

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time; however, it would be difficult to draw appropriate conclusions, particularly when the observed differences in outcome measures between treated and control groups are relatively small. This type of approach has been proposed in particular for symptomatic treatments; however, patients with different SCA genotypes may have a different age at onset, different rate of disease progression, and different types of extracerebellar manifestations. For this reason, the enrollment of subjects with the same SCA genotype would allow a better control for confounding variables. For trials on disease-modifying agents and even more so for trials on genotype-­ specific interventions, the approach of including patients with different SCA genotypes will be clearly inappropriate. For example in the most frequent polyglutamine SCAs, the annual increase in SARA, a scale specifically developed for SCAs (Schmitz-Hübsch et al. 2006), was found to be different. Patients with SCA1 have the fastest rate of disease progression with annual SARA score increase of 2.1; in SCA2 patients there is an increase of 1.49; in SCA3 patients of 1.56, while SCA6 patients have the lowest rate of progression with an annual increase of 0.80 (Jacobi et al. 2015). Furthermore to the core cerebellar symptoms, there is still significant variability in clinical presentations and progression of extra-cerebellar manifestations for SCA patients that may contribute in the overall assessment of the clinical status of the patients (Ashizawa et al. 2018). Each SCA genotype has a specific and characteristic pattern of non-ataxia signs that could influence progression and motor function. The disease stage may also be a variable increasing heterogeneity in the same sample population. This would apply, for example, to variability in symptoms and rate of progression in very early or even pre-symptomatic stages of the disease, and in the late-disease stages when patients may be no longer ambulant. It has also to be considered that upcoming interventional trials would likely test not only symptomatic therapies, but rather disease-modifying therapies requiring matching the candidate drug to a specific patient population. In this scenario, the time of intervention is also extremely important for the success of the therapies. In disease-modifying treatments, a preventive therapy may be more effective in the earliest stages of the disease or, ideally, even before the clinical manifestations. The same type of treatments may be not effective in reversing the disease process in symptomatic patients (Ashizawa et al. 2018). Taking into consideration the points discussed above, the advice for population selection for SCA clinical trial would be to ensure an adequate sample size of patients with the same genotype and with known confounders or predictors.

3.3 Outcome Measures Clinical assessments of cerebellar disease symptoms are usually achieved by clinical rating scales. Several expert centers and consortia have tested in the last decade different validated measures of structural and functional changes, quality of life, and disability

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scales for natural history studies; however, the optimized and most suitable evaluations for clinical trials have not been yet completely established. SARA is the most widely used ataxia scale in SCAs has eight items that yield a total score of 0 (no ataxia) to 40 (most severe ataxia), assessing gait, stance, sitting, speech disturbance, finger chase, nose to finger test, fast alternating hand movements, and heel-shin slide. One of the advantages of SARA scale is that time for completion by a trained health care professional is less than 15  minutes in most cases (Schmitz-Hübsch et al. 2006; Perez-Lloret et al. 2021). In SCAs, the sensitivity of the SARA scale and of other clinical outcome measures has been shown to vary in patients with different genotypes and disease stages. For SARA scale, no significant floor or ceiling effect for total score has been reported, both in validation and natural history studies on a mixed population of SCAs (Schmitz-Hübsch et al. 2006; Jacobi et al. 2015); however, a ceiling effect was later observed for long disease durations (Tanguy Melac et al. 2018). Moreover, SARA scale is not adequate to assess the process of disease progression in pre-symptomatic subjects (Fig. 3). Disease-related changes may start before overt ataxia is visible and may continue in non-ambulatory patients, but these changes are not properly capture by the clinical scale alone. New technological innovations are being investigated in order to remove any bias from a human rater, and to provide objective longitudinal data on patient symptoms. For example, the use of wearable sensors or smartphones to capture

Fig. 3  Measures of disease progression in SCAs. Different measures of changes occurring during disease progression in spinocerebellar ataxias, from the pre-ataxic phase to the onset of manifest clinical symptoms

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movements has been recently proposed for remote tracking of patients’ movements, and secure a record of symptoms on a day-to-day basis (Brooker et al. 2021). Recent findings suggest that magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) biomarkers will provide objective biological readouts of disease activity and progression (Reetz et al. 2013; Adanyeguh et al. 2018; Brooker et al. 2021) (Fig. 3). Structural voxel-based morphometry can detect and longitudinally track regional atrophy not only during the symptomatic phases of SCA diseases, but also during the presymptomatic period close to phenotypic conversion (Jacobi et al. 2020; Nigri et al. 2020, 2022). MRS has also been shown to be an efficient and non-invasive method to analyze regional metabolic differences in both presymptomatic and symptomatic SCA patients. In addition, several investigations are currently testing the possibility that specific biomolecules or metabolites in blood or cerebrospinal fluid (CSF) may correlate with the disease progression. Some of these molecules are disease specific, as for example the mutant proteins of polyglutamine SCAs or other polyglutamine disorders such as Huntington disease. Other proposed biomolecules are common to several neurodegenerative disorders, as for example the neurofilament protein (Khalil et al. 2018). In a recent phase I–IIa clinical trial for Huntington disease, the measurement of the disease-causing protein in CSF has been used as a surrogate exploratory endpoint to evaluate the possible effect of a therapy with antisense oligonucleotides (ASO) (Tabrizi et  al. 2019). The observation of significantly lowered levels of mutant huntingtin in CSF, and the absence of serious side effects, prompted to the design of a large phase III study, with more than 800 patients enrolled worldwide. Unfortunately, a planned review of the data by an independent committee of experts led to the recommendation of an early termination of the trial, concluding that the drug’s potential benefits did not outweigh its risks (Kwon 2021). These results, though disappointing, showed the great importance of considering biomarkers as a useful tool in strict connection with clinical measures centered on patient medical conditions. Very important for study designs are also the patient-­ based reported outcome measures (PROMs). PROMs enable patients to report on their quality of life, daily functioning, symptoms, and may capture other aspects of their health and well-being (Black 2013). In a recent study in SCA3 patients, Maas and colleagues observed discordance between patient-reported and clinician-based outcomes indicating that these measures genuinely evaluate distinct aspects of disease and emphasize their complementariness in therapeutic trials (Maas et al. 2021). The specific features of the currently available outcomes for SCAs have great implications for study design. It is possible that the combined use of both functional clinical scales and structural brain imaging data could improve sensitivity to changes in response to treatment and contribute to more efficient trial design in future trials. Continuous biomarker, like CSF wet biomarkers, atrophy from MRI scans, or quantitative measurement of ataxia should be preferred to clinical outcomes alone. In addition, using repeated measures in the same individual or using continuous outcome variables may enhance statistical efficiency, depending on the properties of the outcome measures (Whicher et al. 2018). To increase compliance and acquire

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more frequent evaluations, a hybrid trial model in which some study visits are done at home, some are carried out in the clinic, and some interactions take place remotely has been found to be preferred by patients and their accompanying family members.

3.4 Trial Designs The main goal of clinical trials is to establish the authentic effect of an intervention separated from possible bias and confounding variables (Evans 2010). RCTs have long been considered the primary research study design because it may provide the best insurance that the results may be due to the intervention; however, to be really informative RCTs need to rely on clear endpoint and rigorous study procedures and sample size calculation. An important aspect of RCT is randomization. Randomization is a crucial tool that helps control for bias in clinical trials favoring the balance between treatment and control with respect to participant characteristics (Evans 2010). In double-blind RCTs, the importance of a control group is highlighted by observation of a clear reduction in the ataxia clinical scores in placebo-treated subjects (Table 1). A reduction in clinical scores, thus indicating an improvement in ataxic symptomatology, is very likely a placebo effect since it is not associated with the natural history of SCA diseases, and is never reported in observational studies (Jacobi et al. 2015). The inclusion of concurrent randomized controls is preferred over the use of historical controls. Historical controls are derived from previously conducted studies and are rarely used in clinical trials performed for drug development because they represent a nonrandomized population. In the absence of concurrent randomization, in fact, it is more likely the occurrence of selection bias with unknown influencing factors on score, progression, and variability that are unequally represented in the treatment arms. Usually, the patient reported in historical control groups (as for example in natural history studies for poly-glutamine SCAs) had worse outcomes than patients participating in clinical trials in the control groups (see Table 1) (Dawson-Saunders and Trapp 2004). For this reason, the use of historical controls in interventional trials may support erroneous conclusions misleading often in favor of the tested therapies. In addition, a well-design trial should consider stratification based on variables that are expected to impact on the observed outcome and thus also on the response to treatment. In stratified randomization, separate randomization schedules should be prepared for each of the confounding variables. For example, in polyQ patients it would be important to ensure a correct balance in the treatment arms of several variables that can have an impact on the outcome, such as the baseline scores at the ataxia rating scale, the length of the pathological triplet expansions, and presence and severity of neurological symptoms. The stratified assignment to participant groups ensures an equal number of subjects with low or high clinical scores in both arms of the trial, and that early onset or

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late onset patients are equally distributed. The problem with stratified trial design is that the sample size has to be large enough in order to be able to enroll an adequate number of patients for each treatment and for each stratum that will be subsequently analyzed. Adaptive trial design represents a more recent strategy that can be applied to RCT. Adaptive trial designs may provide interesting options in a rare condition in order to reduce the number of subjects and speed up therapeutic deployment (Bhatt and Mehta 2016). In adaptive study designs, the sample size may be reduced by including multiple treatment options in a factorial study, in which two (or more) treatment comparisons are carried out simultaneously and compared with a unique placebo-controlled group. The design of seamless confirmatory randomized phase II/III studies could allow fewer patients and an overall shorter duration in respect to traditional RCT accomplishing phase II and phase III as separated studies. Other form of adaptive trial designs is being considered also for rare neurological diseases as a way to speed up the process for drug approval. Aim of these designs is to improve efficiency and to standardize procedures in the development and evaluation of different interventions under a common infrastructure. These new designs can be classified into “basket trials,” “umbrella trials,” and “platform trials” (Park et  al. 2019). In basket trials, a targeted therapy is evaluated on multiple diseases that have common genetic or molecular background; this type of design may prove useful, for example, to assess simultaneously the same compound in different polyQ SCAs. Conversely, umbrella trials evaluate multiple therapies for a single disease, stratified according to molecular alteration or disease biomarker. Finally, platform trials can be described as multi-arm, multi-stage trials (Adaptive Platform Trials Coalition 2019). These trials aim to evaluate several interventions compared to a common control group, can be used continuously, and can be, hypothetically, perpetual. This design allows pre-specified adaptation rules for dropping ineffective treatments and for adding new intervention during the trial. Platform trials seem particularly suited when the population that can be enrolled is small, as in rare diseases.

3.5 Clinical Trial in Preclinical Stage SCA In inherited adult-onset neurodegenerative disorders, such as SCAs, the possibility of predictive testing in at-risk family members allows the identification of mutation carriers several years or even decades before the manifestation of clinical symptoms (pre-symptomatic phase). The definition of “pre-symptomatic” stage implies an objective criterion or threshold for the first recognition of ataxia symptoms to be used for inclusion criteria in clinical trials. It has been proposed the use of SARA scale, the validation of which indicated that a score of 3 or more differentiates controls from SCA patients with manifest ataxia (Schmitz-Hübsch et al. 2006). However, carriers of SCA mutation may present neurological, neuroimaging, or neurophysiological signs, indicating neurodegeneration, before any ataxia symptoms could be identified (Fig. 3). For

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this reason, the term pre-clinical stage has been preferred for the disease period in which no symptoms are observed but paraclinical tests may reveal the presence of neuropathological processes (Maas et al. 2015). SCA mutation carriers not presenting either clinical or paraclinical abnormalities could be identified as asymptomatic carriers and this condition will start from birth until the observation of paraclinical abnormalities. Large multicenter studies on preclinical polyQ SCAs mutation carriers have provided the bases for the creation of mathematical models enabling the prediction of age of onset from the length of CAG repeat region, and in some model, from the age of onset of the affected parents (Globas et al. 2008; Velázquez-Pérez et  al. 2011b; Tezenas du Montcel et  al. 2014). The estimated age of onset could represent a valuable parameter in order to enroll subjects having homogeneous characteristics in respect to expected ataxia manifestations. A critical issue for the design of clinical trials in preclinical subjects is the absence of validate outcomes that efficiently and rapidly could measure disease progression and reflect the effects of an intervention. Presently, the more promising tools are represented by MRI structural analyses of specific areas of atrophy, such as in cerebellum and brainstem, that are already identified in preclinical phase and progress over time (Reetz et al. 2018; Nigri et al. 2020, 2022). In addition, neurofilament light (NfL) represents a valuable biomarker of neurodegeneration in polyQ SCAs. Blood levels of NfL have been found increased at the ataxic stage and already at the presymptomatic stage, as compared with healthy controls (Wilke et al. 2018, 2022; Peng et al. 2020; Yan et al. 2021; Coarelli et al. 2021). In the presymptomatic stage, the NfL increases with proximity to the expected onset, being associated with early neurodegeneration, and even predicting cerebellar volumetric changes (Coarelli et al. 2021; Peng et al. 2022). For trials in carriers, NfL levels could help in stratifying the subjects on the basis of their proximity to disease onset, and could be used to monitor possible changes occurring in response to treatment (Coarelli et al. 2021; Wilke et al. 2022). In last years, regulatory agencies, namely EMA and FDA (EMA 2014; FDA 2013), have already considered the possibility of designing trials to treat subjects at risk of developing neurodegenerative diseases, in particular Alzheimer disease and other dementias. However, there are fundamental issue that need to be further addressed, as, for example, when to start a potentially disease modifying treatment, how many years a clinical trial should last, how to correctly calculate sample size. Future trials testing gene therapy for specific SCA genotypes might certainly require patients at an early stage of disease, including preclinical subjects. Treating neurodegenerative conditions before overt symptoms onset may be more effective in limiting the progression of the neurodegenerative process and, therefore, have a more pronounced effect in ameliorating patient quality of life and life-expectancy (Ashizawa et al. 2018).

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4 Conclusions Undoubtedly, inherited cerebellar ataxias need the development of efficacious therapeutic approaches both symptomatic and disease-modifying. The prompt demonstration of the effectiveness of new interventions needs the design of efficient clinical trials. One obstacle to develop precise treatments for SCAs is the diversity of causes of the condition. Mutations in more than 40 genes can result in SCA and there are likely hundreds of disease-causing mutations (Bushart et al. 2016). The over 20-year experience in pharmacological and nonpharmacological intervention in patients with SCAs had led to important acquisitions and understanding on how to approach clinical trial design. In addition, international disease registries provided accurate knowledge of the natural history for the most frequent SCA genotypes, and will certainly play a fundamental role in assisting and facilitating enrolment in future multi-center trials. The most important gap to be filled for the implementation of successful trials in SCA remains the availability of reliable and sensitive outcome measures. It is likely that none of the currently used clinical scales for ataxia will be sufficient as a single tool. It seems very plausible that the appropriate combination of neuroimaging structural data, peripheral biomarkers, digital measures, and patient reported outcome could represent more valuable instruments to follow disease progression and response to therapies in interventional trials. Upcoming clinical trials most likely will focus on single ataxia genotype and the possible intervention will be specific for the causative genetic defect, allowing clinically relevant and clearly recognizable improvements in patient’s life.

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Therapy Development for Spinocerebellar Ataxia: Rating Scales and Biomarkers Chih-Chun Lin and Sheng-Han Kuo

Abstract  Spinocerebellar ataxias (SCAs) are a group of dominantly inherited disorders with progressive cerebellar dysfunction. Although there are no Food and Drug Administration-approved therapies in the United States for SCAs, the efforts in the past decades have helped us gain an understanding of the pathomechanisms and disease progression, especially with cytosine-adenine-guanine (CAG) expansion SCAs. This has set the stage for the development of symptomatic or diseasemodifying therapies. However, when designing clinical trials, it is important to choose suitable clinical rating scales to monitor disease progression and response to therapeutic interventions. In addition, studies need to incorporate appropriate biomarkers that can be used to test for target engagement. This chapter will review the rating scales and recent advances of biomarkers, focusing on CAG-repeat SCAs. Understanding these available tools will facilitate the design of clinical trials to find therapies for SCAs. Keywords  Rating scales · Biomarkers · Physiology · Imaging

1 Introduction CAG-repeat spinocerebellar ataxias (SCAs) include SCA1, 2, 3, 6, 7, and 17, are most common among the 48 subtypes of SCAs, and their clinical progression has been characterized in the natural history studies in the United States (Ashizawa et al. 2013), Europe (Jacobi et al. 2011), Japan (Sasaki et al. 1996; Yasui et al. 2014), C.-C. Lin · S.-H. Kuo (*) Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA Initiative for Columbia Ataxia and Tremor, Columbia University, New York, NY, USA e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_9

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Brazil (Franca Jr. et al. 2009; Rezende et al. 2018; Piccinin et al. 2020), Portugal (Mendonca et al. 2018), Taiwan (Lee et al. 2011), and China (Guo et al. 2020). The well-studied disease progression and high disease penetrance make CAG-repeat SCAs good candidates for clinical trials studying gene therapies or anti-sense oligonucleotides (ASOs). All clinical trials for SCAs have an inherent challenge in recruiting a sufficient number of patients, owing to its rarity with a collective prevalence of 1–6 per 100,000 (Ashizawa et  al. 2018). Furthermore, in addition to the core cerebellar symptoms, there is still significant variability in clinical presentations of extra-­ cerebellar manifestations for SCA patients. To solve these challenges, several international collaborations and consortiums have been set up to study the natural history and diverse clinical features of SCAs with validated clinical rating scales. Most of the rating scales for ataxia evaluate various neurological symptoms in different body parts to gauge the ataxia severity. Repeated assessment of the ataxia severity provides information on disease progression in natural history studies and responsiveness to therapy in clinical trials. In addition, there are rating scales to measure the non-ataxic symptoms and non-motor features of SCAs. Finally, rating scales are developed to assess the functional status, including activities of daily living of SCA patients. Other than rating scales as endpoints for clinical trials, biomarkers are also key to trial success. The development of biomarkers includes three aspects: neuroimaging, fluid, and physiology (Fig. 1). Each biomarker serves a unique purpose to provide an objective measurement of a specific aspect of the disease. Among these, biomarkers for tracking disease progression and testing for target engagement are the main focus of research. This chapter will review the existing clinical rating scales and recent developments in biomarkers for SCAs.

Fig. 1  The summary of biomarkers of SCAs, including clinical rating scales, neuroimaging, and biofluid biomarkers. NF-L: neurofilament light chain. See text for other abbreviations

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2 Rating Scales (Table 1) Most of the clinical rating scales for SCAs focus on assessing the cerebellar dysfunction to measure the disease severity, which is often the primary endpoint for clinical trials for SCAs. However, some SCAs present with neurological symptoms other than ataxia. Therefore, rating scales capturing the full motor symptoms of Table 1  Rating scales for spinocerebellar ataxias Rating scale Motor International Cooperative Ataxia Rating Scale (ICARS) Scale for the assessment and rating of ataxia (SARA)

Key features (score range)

Evaluate ataxia motor symptoms, including Trouillas et al. oculomotor examination (0–100) (1997)

Most extensively adopted scale for ataxia with 8 domains assessed but no oculomotor evaluation (0–40) Brief Ataxia Rating Scale A concise 5-domain scale, including (BARS) oculomotor evaluation (0–30) Originally developed for SCA3 but later Neurological Examination Score for the Assessment of validated in SCA2; includes assessments for cerebellar and extra-cerebellar domains: Spinocerebellar Ataxia neuropathy, parkinsonism, and pyramidal (NESSCA) signs (0–40) The Inventory of Non-Ataxia Evaluate extra-cerebellar symptoms Symptoms (INAS) associated with SCA patients; part of the scale is subjective, patient reported outcomes (0–16). Performance Consists of two performance-based tasks: Composite Cerebellar 9-hole peg test and click test Functional Severity Score (CCFS) SCA Functional Index Consists of three functional measures: (SCAFI) timed 8-m walk, 9-hole peg test, and PATA repetition Cognitive Assess the cognitive function of ataxia Cerebellar Cognitive patients Affective Syndrome Scale (CCAS) Functional UHDRS IV Patient reported functional capacity

EQ-5D Patient Health Questionnaire-9 (PHQ-9)

Reference

Schmitz-Hubsch et al. (2006) Schmahmann et al. (2009) Kieling et al. (2008) and Monte et al. (2017)

Jacobi et al. (2013a)

du Montcel et al. (2008) Schmitz-Hubsch et al. (2008b)

Hoche et al. (2018)

Unified Huntington’s Disease Rating Scale (1996) Patient reported functional capacity and du Montcel et al. overall health (2008) A self-administered questionnaire to assess Kroenke et al. the severity of depression (2001)

Modified from Chen et al. (2021)

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SCAs are also necessary. Finally, it is vital to have rating scales track cognitive impairment associated with cerebellar dysfunction. We will discuss each in detail.

2.1 Scales for Motor Dysfunction International Cooperative Ataxia Rating Scale (ICARS) is the first rating scale developed for ataxia, introduced in 1997 by the Ataxia Neuropharmacology Committee of the World Federation of Neurology (Trouillas et al. 1997). ICARS measures the severity of ataxia with 19 items, a total score of 100 divided into 4 subscales: posture and gait disturbances, limb ataxia (kinetic functions), dysarthria (speech disturbances), and oculomotor disorders (Trouillas et al. 1997). However, there is redundancy in the subscales, leading to the development of more concise rating scales, such as the Scale for the Assessment and Rating of Ataxia (SARA) (Schmitz-Hubsch et al. 2006). SARA comprises 8 rating items (gait, stance, sitting, speech, finger chase, nose-finger test, fast alternating hand movements, and heel-­ shin slide) with a total score of 40. However, SARA does not include an item to assess ocular abnormalities. To balance the limitations of ICARS and SARA, the Brief Ataxia Rating Scale (BARS) was derived from a modified version of ICARS. BARS consists of only five items: gait, kinetic function of legs and arms, speech, and eye movements, with a total score of 30 (Schmahmann et al. 2009). BARS also has more levels for each item compared with ICARS. Although cerebellar symptoms remain the hallmark of SCAs, it is common to find neurological symptoms outside the cerebellar domain. The Neurological Examination Score for the Assessment of Spinocerebellar Ataxia (NESSCA) was initially developed to assess individuals with SCA3 (Kieling et al. 2008) and later validated in SCA2 (Monte et al. 2017). NESSCA assesses not only ataxia symptoms but also non-ataxia motor symptoms: eyelid retraction, fasciculations, sensory loss, blepharospasm, rigidity, bradykinesia, distal amyotrophy, sphincter dysfunction, vertigo, and optic atrophy. The Inventory of Non-Ataxia Symptoms (INAS), on the other hand, focuses mainly on the non-cerebellar neurological signs, such as spasticity, fasciculations, myoclonus, tremor, dystonia, and vibratory sense. Oculomotor abnormalities, such as nystagmus and hypo- or hyper-metric saccades, are also included, which can also be the result of cerebellar pathology (Jacobi et al. 2013a). Including INAS in a clinical trial is helpful in monitoring the progression of the non-ataxia motor symptoms in SCA patients. Among these rating scales, SARA has become the most adopted rating scale in clinical studies for SCAs to assess the core cerebellar symptoms since its introduction in 2006. SARA has been extensively validated with an excellent inter-rater reliability (interclass coefficient = 0.98) and test-retest reliability (interclass coefficient = 0.90) (Schmitz-Hubsch et al. 2006). Importantly, the natural history studies of SCA1, 2, 3, and 6 in the cohorts in Europe and the United States adopted SARA as the primary rating scale (Ashizawa et al. 2013; Jacobi et al. 2011; Diallo et al. 2018; Moriarty et  al. 2016; Jacobi et  al. 2015; Schmitz-Hubsch et  al. 2008a),

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demonstrating SARA scores progress linearly in these SCAs. However, the rates of progression differ among different types of SCA, as demonstrated by both SARA and INAS in patients with SCA1, 2, 3, 6, and 17 (Ashizawa et al. 2013; Jacobi et al. 2011; Yasui et al. 2014; Piccinin et al. 2020; Lee et al. 2011) (Table 2). There also appears to be a geographical difference in the rate of progression measured by SARA, even within the same type of SCA (Table 2). Recently, SARAhome, a video-based scale modified from SARA, was designed to assess ataxia severity at home to capture day-to-day and within-day fluctuations (Grobe-Einsler et al. 2021). SARAhome includes 5 items from SARA (gait, stance, speech, nose-finger test, and fast alternating hand movements) with scores ranging from 0 to 28. To validate SARAhome, SARA scores, measured in neurology clinics, were compared to SARAhome, captured by videos. The scores for SARAhome correlated highly and progressed in parallel with the total SARA scores (Grobe-Einsler et al. 2021). In addition, SARAhome also demonstrated its ability to capture the variability of ataxia severity over the period of two weeks, allowing repeated measures for more accurate ataxia assessment (Grobe-Einsler et al. 2021). Table 2  Rate of progression measured by SARA and INAS Diagnosis The annual SARA increment SCA1 1.61 ± 0.41 (Ashizawa et al. 2013) 2.18 ± 0.17 (Jacobi et al. 2011)

The annual INAS increment 0.56 ± 0.11 (Jacobi et al. 2011)

SCA2

0.71 ± 0.31 (Ashizawa et al. 2013) 1.40 ± 0.11 (Jacobi et al. 2011) 2.88 ± 2.32 (Lee et al. 2011)

0.30 ± 0.08 (Jacobi et al. 2011)

SCA3

0.65 ± 0.24 (Ashizawa et al. 2013) 1.61 ± 0.12 (Jacobi et al. 2011) 0.71 (Piccinin et al. 2020) 3.00 ± 1.52 (Lee et al. 2011)

0.30 ± 0.08 (Jacobi et al. 2011)

SCA6

0.87 ± 0.28 (Ashizawa et al. 2013) 0.35 ± 0.34 for the first year (Jacobi et al. 2011) 1.44 ± 0.34 for the second year (Jacobi et al. 2011) 1.33 ± 1.40 (Yasui et al. 2014) 2.04 ± 0.76 (Lee et al. 2011) 4.50 ± 2.22 (Lee et al. 2011)

0.10 ± 0.08 (Jacobi et al. 2011)

SCA17

Location United States (Ashizawa et al. 2013) Europe (Jacobi et al. 2011) United States (Ashizawa et al. 2013) Europe (Jacobi et al. 2011) Taiwan (Lee et al. 2011) United States (Ashizawa et al. 2013) Europe (Jacobi et al. 2011) Brazil (Piccinin et al. 2020) Taiwan (Lee et al. 2011) United States (Ashizawa et al. 2013) Europe (Jacobi et al. 2011) Japan (Yasui et al. 2014) Taiwan (Lee et al. 2011)

Taiwan (Lee et al. 2011)

Modified from Chen et al. (2021) Values are given as mean ± SE INAS The Inventory of Non-Ataxia Symptoms, SARA scale for the assessment and rating of ataxia

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2.2 Scales for Performance The items in the rating scales mentioned above typically evaluate an isolated neurological function, such as a specific cerebellar function or the strength of a specific muscle group. However, each daily activity encountered may require a combination of several neurological functions. To better assess the functional performance of SCA patients, two performance-based rating scales are commonly used, the Composite Cerebellar Functional Severity Score (CCFS) (du Montcel et al. 2008) and the SCA Functional Index (SCAFI) (Schmitz-Hubsch et  al. 2008b). CCFS includes the 9-peg board test and the click test. The former measures the time for a patient to place 9 pegs into holes, and the latter measures how fast a patient can press two buttons alternatively for 10 times. CCFS measures the severity of appendicular ataxia but only the upper extremities. SCAFI extends the measurement to the lower extremities by adding a timed 8-m walk to assess the combination of lower limb function and balance. However, this test is only applicable to patients who can still ambulate, whether assistive devices were used. Furthermore, the use of different assistive devices cannot be accounted for in data analysis. An important feature for CCFS is that age should be considered in these performance tests since older healthy controls generally performed worse than the younger healthy controls (du Montcel et al. 2008). Therefore, age adjustment is needed. Another interesting point is that CCFS does not appear to be influenced by depressed mood (du Montcel et al. 2008), which is common among SCA patients (Lo et al. 2016). In summary, both CCFS and SCAFI demonstrate the real-world performance of SCA patients in activities involving coordination.

2.3 Scales for Non-motor Symptoms The cerebellum projects extensively to various areas of the cerebral cortex to modulate cerebral function. As a result, dysfunction of the cerebellum can lead to a variety of cognitive symptoms in addition to motor impairments in patients with SCAs. To evaluate the cognitive dysfunction in patients with cerebellar ataxia, the Cerebellar Cognitive Affective Syndrome Scale (CCAS) assesses several domains of cognitive function (semantic fluency, phonemic fluency, category switching, verbal registration, digit span, cube drawing/copying, recalls, similarities, go-no-go, and affect) (Hoche et  al. 2018). Monitoring cognitive dysfunction is particularly important, because impairments of these cognitive functions can significantly impact the quality of life in SCA patients. Depression has been frequently reported in SCA3 (Kawai et al. 2004; McMurtray et al. 2006; Braga-Neto et al. 2012) and is one of the most commonly identified non-­ motor symptoms in patients with SCAs (Schmitz-Hubsch et al. 2011). The clinical studies in both Europe (Schmitz-Hubsch et al. 2011) and the United States (Lo et al. 2016), such as the EUROSCA and CRC-SCA natural history study (Lo et al. 2016;

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Schmitz-Hubsch et al. 2011), adopted the Patient Health Questionnaire-9 (PHQ-9), a self-administered questionnaire, to assess the severity of depression (Kroenke et al. 2001). This is a 9-item questionnaire, with each item having four levels of scores with increasing severity (0–3), hence a maximal total score of 27. Mild, moderate, moderately severe, and severe depression correspond to 5, 10, 15, and 20 points (Kroenke et al. 2001).

2.4 Scales for Functional Capacity and Quality of Life Ataxia researchers frequently measure the functional status of a patient with the following two scales, the Part IV of the Unified Huntington’s Disease Rating Scale (UHDRS IV) and the EQ-5D. UHDRS IV measures the functional capacity with 25 questions to document the patient’s capabilities in activities of daily living, handling financial matters, and performing at work (Unified Huntington’s Disease Rating Scale 1996). EQ-5D measures both the functional level and the overall health of a patient (du Montcel et al. 2008). The version commonly used is EQ-5D-3L, which includes five 3-level questions to assess the patient’s mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. An additional self-reported score between 0 and 100 reflects the patient’s overall health state (Hurst et  al. 1997). UHDRS IV and EQ-5D are important because they may reflect the response to treatment from the patient’s perspective. In fact, there have been doubts that even if an improvement was found by rating scales based on neurological examinations, such as SARA, the patient might not find a perceivable change in his/her day-to-day experience. As a result, the inclusion of outcome measurements that reflect functional improvements reported by patients has been requested by the Food and Drug Administration in the United States in the design of clinical trials (Health USDo, Human Services FDACfDE, Research et al. 2006; Mercieca-Bebber et al. 2018).

3 Biomarkers 3.1 Neuroimaging Biomarkers Neuroimaging has been one of the most studied modalities in the search for biomarkers for SCAs, because techniques such as magnetic resonance imaging (MRI) can detect atrophy of the cerebellum, a common finding in SCAs as the result of cerebellar degeneration. In addition, metabolic alterations of the cerebellum and related brainstem areas can be identified by positron emission tomography (PET) or magnetic resonance spectroscopy (MRS), often preceding structural changes. Single-photon emission computed tomography (SPECT) can assess the reserve of dopaminergic and GABAergic neurotransmission and cerebral perfusion. The neuroimage findings are summarized in Table 3.

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Table 3  Major image findings Modality Major findings MRI SCA1 ↓cerebellum, brainstem (Guerrini et al. 2004; Schulz et al. 2010), caudate (Schulz et al. 2010), putamen (Schulz et al. 2010), and temporal lobe (Schulz et al. 2010) ↓WM in cerebellar hemispheres (Goel et al. 2011) ↓Spinal cord (Martins Jr. et al. 2017) SCA2 ↓cerebellum (Guerrini et al. 2004; Reetz et al. 2018; Goel et al. 2011), brainstem (Guerrini et al. 2004; Reetz et al. 2018) ↓fractional anisotropy and mode of anisotropy in the brain stem, cerebellar peduncles, cerebellum, cerebral hemisphere WM, corpus callosum, and thalami (Mascalchi et al. 2015) SCA3 ↓cerebellum (Eichler et al. 2011; Etchebehere et al. 2001; Reetz et al. 2013; Schulz et al. 2010), deep cerebellar nuclei (Stefanescu et al. 2015), brainstem (Eichler et al. 2011; Reetz et al. 2013; Schulz et al. 2010), spinal cord (Faber et al. 2021; Fahl et al. 2015), basal ganglia (Reetz et al. 2013; Schulz et al. 2010), and temporal lobe (Schulz et al. 2010) ↓WM in cerebellum (Guimaraes et al. 2013), cerebellar hemispheres (Kang et al. 2014), brainstem (Guimaraes et al. 2013), bilateral thalamus (Kang et al. 2014) ↓fractional anisotropy in cerebellum (Guimaraes et al. 2013), brainstem (Guimaraes et al. 2013) SCA6 ↓cerebellum (Stefanescu et al. 2015; Eichler et al. 2011; Reetz et al. 2013; Schulz et al. 2010), deep cerebellar nuclei (Stefanescu et al. 2015), brainstem (Eichler et al. 2011; Reetz et al. 2013; Schulz et al. 2010), basal ganglia (Reetz et al. 2013) SCA17 ↓cerebellum (Brockmann et al. 2012; Reetz et al. 2010), caudate nucleus (Brockmann et al. 2012), limbic system and parietal precuneus (Reetz et al. 2010) MRS SCA1 ↓Glu, NAA, NAAG, tNAA, Cho/Cr, Glu/Gln, NAA/Cho, NAA/Cr (Guerrini et al. 2004; Lirng et al. 2012; Oz et al. 2010, 2011; Joers et al. 2018; Doss et al. 2014; Lirng et al. 2012) ↑Glc, Gln, mI, Tau, tCr, Glc+Tau (Oz et al. 2010; Oz et al. 2011; Joers et al. 2018) SCA2 ↓Cho, Glu, NAA, tNAA, Cho/Cr, NAA/Cho, NAA/Cr (Guerrini et al. 2004; Lirng et al. 2012; Viau et al. 2005; Oz et al. 2011; Wang et al. 2012) ↑Gln, GSH, mI, Tau, tCr, Glc+Tau, mI/Cr (Viau et al. 2005; Oz et al. 2011; Joers et al. 2018) SCA3 ↓NAA, NAAG, tNAA, NAA/Cho, NAA/Cr (Joers et al. 2018; Wang et al. 2012; Huang et al. 2017) ↑mI, Tau, tCr, Glc+Tau (Joers et al. 2018) SCA6 ↓GABA, NAA, tNAA, NAA/Cho, NAA/Cr (Joers et al. 2018) ↑Lac mI, Glc+Tau (Oz et al. 2011; Joers et al. 2018) SCA17 ↓NAA/Cho, NAA/Cr (Lirng et al. 2012) fMRI ↓cerebellar cortex and deep cerebellar nuclei (Stefanescu et al. 2015) (continued)

Table 3 (continued) Modality Major findings PET SCA1 [18F]FDG:↓metabolism in cerebellum (Wullner et al. 2005), brainstem (Gilman et al. 1996; Wullner et al. 2005), cerebral cortex, caudate nucleus, putamen, thalamus (Gilman et al. 1996) SCA2 [18F]FDG:↓metabolism in cerebellum (Wang et al. 2007; Wullner et al. 2005; Oh et al. 2017), brainstem (Wang et al. 2007; Wullner et al. 2005), parietal cortex (Wullner et al. 2005), parahippocampal gyrus (Wang et al. 2007), frontal cortex (Wang et al. 2007) [11C]dMP:↓Dopamine transporter levels in putamen and caudate nucleus (Wullner et al. 2005) SCA3 [18F]FDG:↓metabolism in cerebellum (Wang et al. 2007; Wullner et al. 2005; Soong et al. 1997; Soong and Liu 1998), brainstem (Wullner et al. 2005; Soong et al. 1997; Soong and Liu 1998), occipital cortex (Soong et al. 1997; Soong and Liu 1998), basal ganglia (Wang et al. 2007; Wullner et al. 2005), thalamus (Wullner et al. 2005), parahippocampal gyrus (Wang et al. 2007),↑metabolism in parietal and temporal cortices preclinically (Soong and Liu 1998) [11C]dMP:↓Dopamine transporter levels in basal ganglia (Wullner et al. 2005) [11C]MP4P:↓thalamus (Hirano et al. 2008) SCA6 [18F]FDG:↓metabolism in cerebellum (Wang et al. 2007; Wullner et al. 2005; Oh et al. 2017; Soong et al. 2001), brainstem (Soong et al. 2001), basal ganglia (Wullner et al. 2005; Soong et al. 2001), cerebral cortex (Wang et al. 2007; Soong et al. 2001);↑temporal cortex (Wullner et al. 2005) SCA17 [18F]FDG:↓metabolism in basal ganglia (Brockmann et al. 2012) [11C]dMP:↓Dopamine transporter levels in caudate nucleus and putamen (Brockmann et al. 2012) [11C]Raclopride:↓D2 receptor levels in caudate nucleus and putamen (Brockmann et al. 2012) SPECT SCA2 [99mTc]TRODAT-1 SPECT:↓striatal DAT binding (Yun et al. 2011) [123I]β-CΙΤ SPECT:↓striato-cerebellar ratio (Boesch et al. 2004) [123I]IBZM SPECT:↓striato-frontal IBZM binding ratio (Boesch et al. 2004) [123I]FP-CIT SPECT: ↓uptake in caudate, putamen (Varrone et al. 2004) SCA3 [99mTc]TRODAT-1 SPECT:↓nigrostriatal ratio (Yen et al. 2000) [99mTc]HMPAO SPECT:↓perfusion in cerebellar hemispheres (Etchebehere et al. 2001), inferior (Etchebehere et al. 2001) and superior (Etchebehere et al. 2001) frontal lobe (Etchebehere et al. 2001), lateral temporal lobe (Etchebehere et al. 2001), parietal lobe (Etchebehere et al. 2001), vermis (Etchebehere et al. 2001) [99mTc]ECD SPECT:↓perfusion in bilateral cerebellum (Braga-Neto et al. 2016), vermis (Braga-Neto et al. 2016) [123I]iomazenil SPECT:↓binding in cerebellum (Ishibashi et al. 1998), cerebral cortex (Ishibashi et al. 1998), thalamus (Ishibashi et al. 1998), striatum (Ishibashi et al. 1998) SCA6 [99mTc]ECD SPECT:↓perfusion in cerebellar hemisphere (Honjo et al. 2004), cerebral vermis (Honjo et al. 2004) SCA17 [99mTc]TRODAT-1 SPECT:↓striatal DAT binding (Yun et al. 2011) Modified from Chen et al. (2021) and Brooker et al. (2021)

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3.1.1 MRI The primary neuroimaging finding in patients with SCAs is the atrophy of the cerebellum. The assessment of the reduction of volume can be done using either region of interest (ROI)-based analysis (Guerrini et al. 2004; Reetz et al. 2018; Stefanescu et al. 2015; Brockmann et al. 2012; Eichler et al. 2011; Etchebehere et al. 2001) or voxel-based morphometry (VBM) (Reetz et al. 2013; Kang et al. 2014; D’Abreu et al. 2012; Goel et al. 2011; Guimaraes et al. 2013; Reetz et al. 2010; Schulz et al. 2010). Although the cerebellum is the most commonly affected brain region in SCAs, MRI has demonstrated volume changes in the brainstem (Guerrini et  al. 2004; Reetz et al. 2018; Eichler et al. 2011; Reetz et al. 2013; Kang et al. 2014; Guimaraes et al. 2013; Schulz et al. 2010), especially pons (Reetz et al. 2018; Goel et al. 2011; Schulz et al. 2010), and basal ganglia (Reetz et al. 2013). The degree of cerebellar atrophy correlates with the severity of ataxia in SCA1 (Guerrini et al. 2004; Reetz et al. 2013; Goel et al. 2011; Schulz et al. 2010), SCA2 (Guerrini et al. 2004; Reetz et al. 2018; Goel et al. 2011), SCA3 (Stefanescu et al. 2015; Eichler et al. 2011; Etchebehere et al. 2001; Reetz et al. 2013; Kang et al. 2014; D’Abreu et al. 2012; Goel et al. 2011; Guimaraes et al. 2013; Schulz et al. 2010), SCA6 (Stefanescu et al. 2015; Eichler et al. 2011; Reetz et al. 2013; Schulz et  al. 2010), and SCA17 (Brockmann et  al. 2012; Reetz et  al. 2010). The cross-­ section area of the spinal cord at C2 and C3 levels also negatively correlate with SARA severity in SCA1 (Martins Jr. et al. 2017). These findings indicate that the volume of the cerebellum and other parts of the central nervous system may be used to track disease progression in SCAs. Can these neuroimaging findings identify structural changes prior to the symptom onset? Indeed, MRI showed volume reduction in the cerebellum and brainstem in pre-symptomatic SCA1 (Jacobi et al. 2013b) and SCA2 (Reetz et al. 2018; Jacobi et  al. 2013b; Nigri et  al. 2020) and the cerebellum and caudate nucleus in pre-­ symptomatic SCA17 (Brockmann et al. 2012). A reduction of the spinal cord area at C2 and C3 levels can also be detected in subjects with pre-symptomatic and symptomatic SCA3 (Faber et al. 2021). Notably, the degree of reduction is more severe in symptomatic SCA3 patients. Therefore, MRI can be more sensitive than clinical rating scales to measure disease progression, which has been demonstrated in SCA1, SCA2, SCA3, and SCA7 (Adanyeguh et al. 2018). The ability to detect changes in the pre-symptomatic stage allows studies to test for disease-modifying therapies before symptom onset. In addition to grey matter visualized by volume analysis, white matter is also studied in SCAs. For example, diffusion tensor imaging showed white matter involvement finding loss of fraction anisotropy in SCA2 and SCA3 (Guimaraes et al. 2013; Mascalchi et al. 2015). The patterns of cerebellar degeneration in particular cerebellar lobules seem to differ among SCAs (Guerrini et al. 2004; Stefanescu et al. 2015; Reetz et al. 2013; Wang et al. 2007). Therefore, a detailed analysis of each lobule may provide information regarding the different degenerative processes of each SCA, for example, lobules VIII and XI are affected more in SCA1 but not in SCA3 and SCA6 (Reetz et al. 2013).

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3.1.2 MRS MRS may detect chemical changes that precede the structural alterations seen in MRI. Commonly studied metabolites include N-acetylaspartate (NAA, a marker of neuronal density and function), creatine/phosphocreatine (Cr, a metabolism marker), choline compounds (Cho, a marker of synthesis and degradation of cell membranes), and myoinositol (a marker for gliosis). The reduction in NAA/Cr and NAA/ Cho ratios thus are markers for neurodegeneration and have been shown in the cerebellum of SCA1 (Mascalchi et  al. 1998; Lirng et  al. 2012), SCA2 (Lirng et  al. 2012; Viau et al. 2005), SCA3 (Lirng et al. 2012; Lei et al. 2011), SCA6 (Lirng et al. 2012; Hadjivassiliou et al. 2012), and SCA17 (Lirng et al. 2012) patients. MRS is an important biomarker because similar biochemical findings in the cerebellum were demonstrated in both patients and a mouse model for SCA1 (Atxn1154Q/2Q). Hence, MRS findings are translatable. In particular, such changes can be tracked spanning the pre-symptomatic and symptomatic stages (Friedrich et  al. 2018). Therefore, therapies that are effective in this mouse model can be studied in clinical trials in SCA1 patients and be monitored with the same MRS biomarkers. 3.1.3 Functional MRI (fMRI) Although fMRI has not been extensively adopted in SCAs, the resting-state fMRI can assess the oxygen consumption of the cerebellum, and it was found to be reduced in the cerebellar cortex and the deep cerebellar nuclei in SCA6 (Stefanescu et al. 2015). 3.1.4 PET PET provides important information in metabolic changes, such as glucose metabolism and integrity of the dopaminergic axis. The PET tracer, [18F]fluorodeoxyglucose ([18F]FDG), measures the tissue uptake of glucose, thus reflecting the overall tissue metabolism. Reduction of [18F]FDG uptake of the cerebellum can be seen in SCA1 (Gilman et al. 1996; Wullner et al. 2005), SCA2 (Wang et al. 2007; Wullner et al. 2005; Oh et al. 2017), SCA3 (Wang et al. 2007; Wullner et al. 2005; Soong et al. 1997; Soong and Liu 1998), SCA6 (Wang et al. 2007; Wullner et al. 2005; Oh et al. 2017; Soong et al. 2001), and in pre-symptomatic SCA3 patients (Soong and Liu 1998). Therefore, PET may be used to monitor the progression of disease in SCA patients (Goel et  al. 2011). Several PET ligands, such as [11C]D-threo-­ methylphenidate or [11C]raclopride, which interrogate the involvement of the dopaminergic axis, can be useful to study SCAs with parkinsonian symptoms, such as SCA2, SCA3, and SCA17 (Brockmann et al. 2012; Wullner et al. 2005).

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3.1.5 SPECT SPECT has been applied in ataxia studies to study the dopaminergic system and overall brain perfusion. SPECT techniques have been used to study dopamine deficiency in Parkinson’s disease, but the dysfunction of dopamine reuptake can also be used to assess the dopamine uptake in SCAs, especially those that can be associated with parkinsonism, such as SCA2 (Yun et al. 2011), SCA3 (Yen et al. 2000), and SCA17 (Yun et al. 2011). [99mTc]ECD SPECT (technetium-99m N,N-1,2-ethylene diylbis-L-cysteine diethyl ester dihydrochloride or ethyl cysteinate dimer) showed a perfusion reduction in SCA3 (Braga-Neto et al. 2016) and SCA6 (Honjo et al. 2004).

3.2 Fluid Biomarkers Although it is still unclear how each mutation in SCAs leads to the clinical phenotype, various biochemical alterations can be detected in blood or cerebrospinal fluid (CSF) (Table 4). These changes can serve as biomarkers for tracking disease progression or testing target engagement in clinical trials studying therapeutic interventions. Similar to many neurodegenerative disorders, patients with SCAs also have axonal degeneration. Tau protein level, a biomarker for axonal degeneration, is reduced in the CSF of SCA2 patients (Brouillette et al. 2015). Neurofilament light chain, another marker for neurodegeneration, is increased in the serum/plasma of SCA1 (Wilke et al. 2018; Coarelli et al. 2021), SCA2 (Coarelli et al. 2021), SCA3 (Wilke et al. 2018; Coarelli et al. 2021; Li et al. 2019; Prudencio et al. 2020; Peng et al. 2020; Wilke et al. 2020), and SCA7 (Coarelli et al. 2021). A study demonstrated similar findings collectively in SCA1, 2, 3, 6, 7, and 17, compared with controls (Shin et al. 2021). The level of neurofilament light chain in CSF is also increased in SCA3 patients (Li et al. 2019; Prudencio et al. 2020). Notably, the levels of neurofilament light chain in the CSF and serum of SCA3 patients correlate with each other (Li et al. 2019), making the neurofilament light chain a peripherally accessible indicator for neurodegeneration in SCA3. In addition, the level of increment can be seen in pre-symptomatic SCA3 patients in CSF (Li et al. 2019) and plasm (Li et al. 2019; Prudencio et  al. 2020). A recent study also demonstrated elevated plasma neurofilament light chain in pre-symptomatic carriers of SCA1, SCA2, SCA3, and SCA7 (Coarelli et al. 2021). Hence the change of neurofilament light chain level precedes the symptom onset. Two studies demonstrated that the serum level of neurofilament light chain is highest in symptomatic SCA3 patients, followed by pre-­ symptomatic SCA3 subjects, and lowest in controls (Peng et al. 2020; Wilke et al. 2020). The elevation of serum neurofilament light chain is estimated to precede the clinical symptoms by 7.5 years (Wilke et al. 2020). Similar findings were identified in a mouse model of SCA3, strengthening the idea that the levels of neurofilament reflect a degenerative process driven by mutant ATXN3 (Wilke et al. 2020). Although the alterations of neurofilament light chain level can be found in other

Table 4  Summary of fluid biomarkers in SCA Biomarker Poly-Q expanded ataxin-3

Catalase activity CHIP Oxidation of DCFH-DA Eotaxin GFAP Glutathione peroxidase activity IGFBP-1 IGFBP-3 IGF-1/IGFBP-3 molar ratio Insulin miRNA

Neurofilament light chain

NSE Phosphorylated neurofilament heavy chain S100B Superoxide dismutase activity Tau Valine, leucine, and tyrosine

Findings ↑ in PBMC of SCA3 (Wilke et al. 2020) ↑ in plasma and CSF of SCA3 (Prudencio et al. 2020) ↑ in PBMC of pre-symptomatic SCA3 (Wilke et al. 2020) ↑ in plasma and CSF of pre-symptomatic SCA3 (Prudencio et al. 2020) ↑ in CSF of symptomatic SCA3 vs. pre-symptomatic SCA3 (Prudencio et al. 2020) ↑ in serum of SCA3 (Pacheco et al. 2013) ↑ in serum of SCA3 (Hu et al. 2019) ↑ in CSF of SCA3 (Hu et al. 2019) ↑ in serum of symptomatic and pre-symptomatic SCA3 (de Assis et al. 2017) ↑ in serum of asymptomatic SCA3 vs. pre-symptomatic SCA3/ controls (da Silva et al. 2016) ↑ in serum of SCA3 (Shi et al. 2015) ↓ in serum of symptomatic and pre-symptomatic SCA3 (de Assis et al. 2017) ↑ in serum of SCA3 (Saute et al. 2011) ↓ in serum of SCA3 (Saute et al. 2011) ↑ in serum of SCA3 (Saute et al. 2011) ↓ in serum of SCA3 (Saute et al. 2011) ↑ miR-34b (Shi et al. 2014) in serum of SCA3 ↑ miR-7014 in CSF of SCA3 (Hou et al. 2019) ↑ 71 miRs in plasma of SCA7 (Borgonio-Cuadra et al. 2019) Alterations of miRs in plasma of early onset SCA7 vs. adult onset SCA7 (Borgonio-Cuadra et al. 2019) ↓ miR-25 (Shi et al. 2014), miR-29a (Shi et al. 2014), miR-125b (Shi et al. 2014) in serum of SCA3 ↓ miR-7014 (Hou et al. 2019) in plasma of SCA3 Different expression of various exosomal miRs in plasma and CSF of SCA3 (Hou et al. 2019) ↑ in serum of SCA1 (Wilke et al. 2018) and SCA3 (Wilke et al. 2018, 2020; Li et al. 2019) ↑ in plasma of pre-symptomatic carriers of SCA1 (Coarelli et al. 2021), SCA2 (Coarelli et al. 2021), SCA3 (Coarelli et al. 2021; Li et al. 2019; Wilke et al. 2020), and SCA7 (Coarelli et al. 2021) ↑ in CSF of SCA3 (Li et al. 2019) ↑ in serum of SCA3 (Zhou et al. 2011; Tort et al. 2005) ↑ in serum of SCA3 (Wilke et al. 2020) ↑ in serum of SCA3 (Zhou et al. 2011) ↓ in serum of symptomatic and pre-symptomatic SCA3 (de Assis et al. 2017) ↑ in CSF of SCA2 (Brouillette et al. 2015) ↓ in plasma of SCA7 (Nambo-Venegas et al. 2020)

Modified from Chen et al. (2021) CHIP carboxyl terminus of the Hsp70-interacting protein, DCFH-DA 2′,7′-dichlorofluorescein diacetate, GFAP glial fibrillary acidic protein, GSH-Px glutathione peroxidase, IGFBP insulin-like growth factor-binding protein, IGF insulin-like growth factor, miRNA microRNA, NSE neuron-­ specific enolase, PBMC peripheral blood mononuclear cell, SOD superoxide dismutase

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neurodegenerative disorders, SCA patients are often in their 30s to 50s and less likely to have other co-existing late-onset neurodegenerative disorders (e.g., Parkinson disease or Alzheimer disease) that may confound the interpretation. Because the expression of abnormal poly-Q is thought to be crucial in the pathogenesis of CAG-repeat SCAs, reducing the expression of poly-Q has been the main goal for gene therapies or ASO-based therapies. To monitor the efficacy and target engagement of such interventions, it is crucial to develop an assay that can detect the level of abnormal poly-Q.  An immunoassay based on time-resolved fluorescence resonance energy transfer can detect abnormal ataxin-3 with expanded poly­Q in blood-derived mononuclear cells harvested from both pre-symptomatic and symptomatic SCA3 patients (Gonsior et  al. 2020). However, this assay can only detect ataxin-3 in the mononuclear cells and not ataxin-3 in the serum or CSF. On the other hand, an electrochemiluminescence immunoassay using the Meso Scale Discovery system (Gendron et al. 2017a; Gendron et al. 2017b) can identify elevated levels of abnormal ataxin-3 in the plasma and CSF in both pre-symptomatic and symptomatic SCA3 patients (Prudencio et  al. 2020). Therefore, the level of ataxin-3 with abnormally expanded poly-Q can serve as a fluid biomarker to test target engagement for SCA3 in clinical trials that reduce the expression of abnormal ataxin-3. Another potential biomarker is an endogenous binding partner of the mutant ataxin-3, the carboxyl terminus of Hsp-70 interacting protein (CHIP), a co-­ chaperone protein. CHIP level is elevated in both serum and CSF of SCA3 patients, indirectly reflecting mutant ataxin-3 level (Hu et al. 2019). Inflammation can occur as the result of neurodegeneration in SCAs. The protein level of an inflammatory cytokine, eotaxin, is elevated in the serum of asymptomatic SCA3 subjects compared to controls and symptomatic SCA3 patients (da Silva et al. 2016). Therefore, eotaxin level can potentially serve as a biomarker to track the transition from the pre-symptomatic to the symptomatic stage in SCA3 patients, which eotaxin level is expected to progressively reduce as the disease progresses. Biomarkers reflecting activation of astrocyte, gliosis, and neuronal damage have been reported in SCA3. Glial fibrillary acidic protein (GFAP), a marker for astrocytes, is elevated in the serum of SCA3 patients (Shi et al. 2014), suggesting astrogliosis occurs in the pathogenesis of SCA3. In agreement with this finding, another marker for astrocyte, S100B, is also increased in the serum of SCA3 patients (Zhou et  al. 2011). Neuron-specific enolase (NSE), a marker for neuronal damage, is increased in the serum of SCA3 patients, and it may be used to track the severity of neurodegeneration in SCA3 (Zhou et al. 2011; Tort et al. 2005). It has been reported that oxidative stress is involved in the pathogenesis of SCA3 (Araujo et al. 2011; Weber et al. 2014; Yu et al. 2009). The activity of catalase, a marker for oxidative stress, is increased in the serum of SCA3 patients (Pacheco et  al. 2013). The oxidation of DCFH-DA, an artificial substrate to measure the degree of oxidation, is increased in the serum of pre-symptomatic SCA3 patients and even higher in symptomatic SCA3 patients (de Assis et al. 2017), suggesting a correlation between oxidative burden and disease severity. On the contrary, the enzymes for clearing oxidative radicals, glutathione peroxidase (de Assis et  al.

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2017), and superoxide dismutase (de Assis et al. 2017), are decreased in the serum of SCA3 patients. The reductions of these two enzymes are also more pronounced in symptomatic SCA3 patients than pre-symptomatic SCA3 patients (de Assis et al. 2017), further supporting the role of oxidative stress in the disease progression. Patients with poly-Q disorders commonly present with insulin resistance, which is thought to result from reduced expression of insulin-like growth factor 1 (IGF-1) due to poly-Q peptides (Craft and Watson 2004). Thus, the levels of IGF-1 and its binding partners, IGFBP1 and IGFBP3, are potential biomarkers for SCAs. SCA3 patients have higher serum levels of IGFBP1 and IGF-1/IGFBP-3 ratio compared with healthy controls, while serum levels of IGFBP-3 and insulin levels are reduced (Saute et al. 2011). Other biomarkers that have been studied include the levels of amino acids and microRNAs (miRNAs). Levels of valine, leucine, and tyrosine are reduced in the plasma of SCA7 patients (Nambo-Venegas et al. 2020). Altered levels of various miRNAs have been reported in serum, plasma, or CSF in SCA3 (Shi et al. 2014; Hou et al. 2019) and SCA7 (Borgonio-Cuadra et al. 2019) patients. The main limitation of the circulating miRNA studies is the small sample sizes. Further validation is necessary. Most fluid biomarkers studies are biased toward SCA3, the most common SCA globally, with only few studies conducted in patients with SCA1, 2, and 7. Whether the findings from SCA3 can be generalized to other SCAs requires validation with future studies.

3.3 Physiology Biomarkers Although the pathology of SCA primarily involves the cerebellum, brain areas receiving projections from the cerebellum may also be affected. Dysfunctions of the corresponding brainstem and cortical regions can be investigated with vestibulo-­ oculography (VOG), brainstem auditory-evoked potential (BAEP), visual-evoked potential (VEP), somatosensory-evoked potential (SSEP), and motor-evoked potential (MEP) (Table 5). VOG can quantitatively measure oculomotor dysfunction in SCAs. For example, the speed of saccade in SCA2 patients has been shown to be around 200 °/s compared with >400 °/s in healthy controls (Buttner et al. 1998). SCA3 patients have prolonged reflex latency (Luis et al. 2016). Patients with SCA1 (Kim et al. 2013), SCA3 (Kim et al. 2013; Wu et al. 2017), and SCA6 (Kim et al. 2013) may have gazeevoked eye nystagmus, dysmetric saccade, and square-wave jerks. These gaze-­ evoked eye movements occur more frequently in symptomatic SCA3 patients compared to pre-symptomatic SCA3 patients (Wu et al. 2017). Gaze-evoked nystagmus is not seen in patients with SCA2 likely because the impaired fast saccade prevents the generation of the saccadic corrective phase of nystagmus (Buttner et al. 1998).

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Table 5  Physiological biomarkers of SCAs Method BAEP

MEP

SSEP

VEP VOG

Findings Prolonged absolute III and V latencies and interpeak I–III latency in SCA2 (Velazquez Perez et al. 2007) Prolonged absolute I and III latency in SCA6 (Kumagai et al. 2000) ↑ CMCT in SCA1 (Jhunjhunwala et al. 2013; Yokota et al. 1998; Schwenkreis et al. 2002), SCA2 (Jhunjhunwala et al. 2013), SCA3 (Jhunjhunwala et al. 2013; Farrar et al. 2016) ↑ RMT in SCA1 (Jhunjhunwala et al. 2013; Yokota et al. 1998; Schwenkreis et al. 2002), SCA3 (Jhunjhunwala et al. 2013) Loss of or prolonged P40 in SCA1 (Abele et al. 1997), SCA2 (Velazquez Perez et al. 2007; Abele et al. 1997), and SCA3 (Abele et al. 1997) Prolonged P40 seen more often in SCA3 (69%) and SCA2 (23%) but not in SCA1 (Abele et al. 1997) Prolonged P100, more commonly seen in SCA1 than SCA3 (Abele et al. 1997) Gaze-evoked nystagmus and dysmetric saccade ↑ in SCA1 (Kim et al. 2013), SCA3 (Kim et al. 2013; Wu et al. 2017), and SCA6 (Kim et al. 2013) Gaze-evoked nystagmus ↑ in symptomatic SCA3 vs. pre-symptomatic SCA3 (Wu et al. 2017) ↓ Saccade velocity in SCA2 (Buttner et al. 1998) SWJ/SWO ↑ in SCA3 (Kim et al. 2013; Wu et al. 2017) Downward nystagmus in SCA6 (Kim et al. 2013) ↑ VOR latency in SCA3 (Luis et al. 2016)

Modified from Chen et al. (2021) BAEP brainstem auditory evoked potential, CMCT central motor conduction time, MEPs motor evoked potential, RMT resting motor threshold, SSEP somatosensory evoked potential, SWJ square-wave jerk, SWO square-wave oscillation, VEP visual evoked potential, VOG video-­ oculography, VOR vestibulo-ocular reflex

Prolonged absolute latencies for peaks III and V in BAEP have been found in SCA2 (Velazquez Perez et al. 2007), while prolonged absolute latencies for peaks I and III have been demonstrated in SCA6 (Kumagai et al. 2000). SSEP demonstrated a disruption of the integrity of the posterior column in SCA1, 2, and 3 patients with prolonged or loss of P40 latency from tibial nerve stimulation. Prolonged P40 latency was seen more commonly in SCA3 (69%) than SCA2 (23%), while not found in SCA1 patients (Abele et al. 1997). Prolonged P100 latency in VEP can be seen in both SCA1 and SCA3 patients, while more frequently occur in SCA1 vs. SCA3 (78% vs. 25%) (Abele et al. 1997). Prolonged central motor conduction time (CMCT) with MEP can be seen in SCA1 (Jhunjhunwala et  al. 2013; Yokota et  al. 1998; Schwenkreis et  al. 2002), SCA2 (Jhunjhunwala et al. 2013), SCA3 (Jhunjhunwala et al. 2013; Farrar et al. 2016), and SCA6 (Lee et  al. 2003), suggesting dysfunctions of the descending motor pathway. Patients with SCA1 (Jhunjhunwala et al. 2013; Yokota et al. 1998; Schwenkreis et al. 2002) and SCA3 (Jhunjhunwala et al. 2013) are found to have increased resting motor threshold (RMT), suggesting reduced corticospinal excitability.

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These physiological assessments can provide objective measures to the function of the brain circuit. However, these tests have not been routinely implemented in clinical practice or clinical trials for SCAs. Most importantly, the correlation between these parameters and neurological symptoms has not been well established. Shall such correlation be validated, incorporating these physiological biomarkers into SCA clinical trial design will be very valuable. Another potential biomarker is error-based learning mediated by the cerebellum, which has been studied by neuroscientists for decades. An example is implementing an error in the visual input to perturb hand-reaching tasks and measuring the rate of error correction by the subject (Gibo et  al. 2013; Criscimagna-Hemminger et  al. 2010; Butcher et al. 2017; Honda et al. 2018, 2020). Although the clinical assessment of finger-to-nose test requires the involvement of error correction, assessment for error-based learning has not been formally implemented. Additionally, efforts have been made to develop quantitative kinematic-based measurements of limb movements and gait in patients with ataxia (Honda et  al. 2018; Lee et  al. 2015; Bhanpuri et al. 2014; Aprigliano et al. 2019; Bakhti et al. 2018; Earhart and Bastian 2001; Hashimoto et al. 2015; Matsuda et al. 2015; Morton and Bastian 2006; Tran et al. 2019). Although the goal is to obtain an objective assessment for disease severity-related physiology measurement, standardized physiological measurements across institutions and perform data analysis will be required and valuable to provide additional information on the overall cerebellar and related brain circuitry.

4 Conclusion This chapter summarizes the rating scales used in CAG-repeat SCAs and the recent development in biomarkers. Rating scales provide clinical assessments, while different biomarkers can deliver objective measurements to imaging, biochemistry, and physiology parameters that may help track disease severity, rate of progression, or therapeutic responses. The natural history studies of SCAs in both the United States (Ashizawa et al. 2013) and Europe (Jacobi et al. 2011) have set the foundation for clinical trial readiness for SCAs (Lin et al. 2020), and several potential therapeutic targets have been identified. Combining the appropriate rating scale and multimodal biomarkers will ensure that clinical trials are designed rigorously with proper clinical assessment, therapeutic interventions indeed engage the expected targets, and the results will identify therapies for patients with SCAs. Funding Information  Dr. Lin has received funding from the National Ataxia Foundation. Dr. Kuo has received funding from the NIH (R01NS104423, R01NS118179, R03NS114871, and R13NS117005), Brain Research Foundation, National Ataxia Foundation, Parkinson’s Foundation, and International Essential Tremor Foundation.

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Clinical Rating Scales for Ataxia Tanja Schmitz-Hübsch

Abstract  Clinical rating scales for ataxia yield a semi-quantitative measure of disease severity. Rating is based on standardized scoring, usually applied on standardized motor tests. Generic ataxia scales such as the International Cooperative Ataxia Rating Scale (ICARS) or the Scale for the Assessment and Rating of Ataxia (SARA) aim to assess ataxia independent of etiology. Disease-specific scales such as Friedreich Ataxia rating Scale (FARS) or the Unified Multiple Systems Atrophy Rating Scale (UMSARS) include a wider spectrum of specific features extending beyond ataxia. For use as an outcome in interventional trials, proof of reliability at retest is a prerequisite and prior data on the evolution of scores over time in the target group are useful for study planning. Remote application by video rating has been explored. Benchmarks of minimally important change or within-study validation against patient report are important to interpret the relevance of observed changes. Additional measures may be applied to capture treatment effects more comprehensively, for example, in the domains of executive functions, affect regulation, fatigue, or autonomic functions. Keywords  Clinical rating · SARA · FARS · ICARS · UMSARS · Responsiveness · Smallest detectable change · Minimally important difference · Timed tests · Videorating

1 Introduction Clinical rating scales aim to describe a disease process based on severity judgements of clinical signs or symptoms by qualified raters, using standardized procedures of both, testing and rating. Thus, clinical rating is closely related to neurological exam per se, which has a prominent role in phenotyping of ataxias. Such phenotype description considers motor signs of cerebellar dysfunction in the first place, but T. Schmitz-Hübsch (*) Experimental and Clinical Research Center, a Cooperation of Max-Delbrueck Center for Molecular Medicine and Charité–Universitätsmedizin Berlin, Berlin, Germany e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_10

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also needs to consider involvement of other structures of central and peripheral nervous system. Consistent with this, clinical rating scales in the field of ataxia can be classified as follows: generic ataxia rating scales that aim to assess severity of ataxia as the common pathology shared by ataxia disorders or disease-specific rating scales which aim to comprehensively assess the clinical manifestation of specific ataxia disorders, for example, Friedreich’s ataxia (FRDA) or Multiple Systems Atrophy cerebellar type (MSA-C), but may not be suitable for other ataxias. Another important notion for clinical rating in ataxia is the wide age spectrum of those affected, which spans from congenital forms to geriatric populations, in which clinical rating may be confounded by motor development or normal aging and comorbidities, respectively. Thus, clinical rating scales for ataxia need to be applicable for clinically heterogeneous populations or otherwise need to specify their target group. Still, dealing with the effects of non-cerebellar signs or comorbidities on test performance remains a challenge in the application not only of clinical rating scales for ataxia but also instrumented assessment of motor functions. Clinical rating scales can be conceived as diagnostic instruments that capture the effects of an underlying disease process at the level of clinically manifest signs and body functions, that is, the impairment level. As a framework for the specific levels addressed, terminology may refer to the international classification of functioning, disability, and health (www.who.int/standards/classifications/international-­ classification-­of-­functioning-­disability-­and-­health), which may also serve to determine the level on which effects of novel therapeutic interventions are expected to occur (Fig. 1). For example, the progressive loss of Purkinje cells or accumulation of intracellular aggregates caused by pathogenic genetic variants may be attributed as the structural correlate of the disease process and measured as loss of cerebellar

Fig. 1  Positioning of ataxia outcomes (middle column, examples or explanation in right column) in relation to the framework of the International Classification of Functioning, Disability and Health (left column)

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volume captured by magnetic resonance imaging (MRI). Clinically, this pathology may result in ataxia and manifest as inability to walk in tandem or difficulty in pointing movement, captured by clinical rating. Patient experience of functional impairment, activity limitations, or restrictions in participation can be conceived as related to disease severity assessed by clinical ratings levels of assessment in fact may show overlap, for example, of walking function and perceived limitations of walking capacity. However, it is important to understand that such relations need not be strong nor linear. For example, remarkable cerebellar atrophy may be present even at the time of first and mild clinical manifestation in some hereditary ataxias and effects of intervention on aggregate formation need not be accompanied by clinical improvement. In line with this, regulatory authorities generally strengthen the use of clinically meaningful primary outcomes for pivotal efficacy trials and as a prerequisite of drug approval. Consequently, clinical rating scales have a pivotal position in this process. They may serve as a primary outcome for interventional trials, if certain criteria are met—see below–or may support the validity of effects observed on other markers, for example, MRI, motor or serum biomarkers. Recent guidance of the Food and Drug Administration for the acceptability of clinical outcome assessments (www.fda.gov/about-­fda/clinical-­outcome-­assessment-­coa-­ frequently-­asked-­questions) stresses consideration of patient experience and patient relevance of changes observed. This challenges the traditional view that amelioration or even prevention of the signs of disease would be of immediate relevance to those affected. However, the assessment of patient experience and its relation to chronic disease progression and clinical ratings in ataxia is still on debate (Riazi et al. 2006; Maas et al. 2021a; Maas and van de Warrenburg 2021)—see below— and patient-report is hitherto generally rare as a primary outcome in movement disorders. This chapter aims to give an overview of clinical rating scales for ataxias and some guidance on the choice of clinical rating scales as trial outcomes. As a supplement to this, it also includes a summary description of complementary measures related to clinical ratings, that is, patient-reported outcomes (PRO), and motor performance measures.

2 Clinical Rating Scales for Ataxia—Remarks on Validation The description includes both general ataxia rating scales and rating scales developed for specific phenotypes. The latter extend assessment and rating beyond the features of cerebellar ataxia to include, for example, features of spasticity and sensory impairment for FRDA (FARS) or Parkinsonian features and autonomic functions for MSA (UMSARS). The different scales are presented with respect to structure and weighting and are sequenced along date of publication. This way, readers may also trace the evolution of validation concepts over time. Results of validation are summed up for the domains of reliability, validity, and responsiveness (Mokkink et al. 2010; Hobart et al. 2007). In this context, internal responsiveness as

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the ability to detect change may be demonstrated by mean score change over timeframes, in which observation of change would be expected, usually 6 months to 2 years in a neurodegenerative disease. Such changes are usually standardized to either standard deviation of baseline, yielding effect sizes (ES), or to standard deviation of change, yielding standardized response mean (SRM). This way the magnitudes of change can be compared between different types of outcomes. Both, ES and SRM, are usually interpreted according to Cohen’s arbitrary criteria as 0.2 = small, 0.5 = moderate, and 0.8 = large internal responsiveness. However, interpretation of effect sizes has to consider some points. When applied in neurodegenerative diseases, effect sizes reflect a feature of the scale (responsiveness) but also a feature of the population (rate of disease progression and score variance within the sample). Further, the score changes observed should be interpreted against two important metrics: smallest detectable change (SDC) and benchmarks for minimal clinically important difference (MID). These metrics need to be defined from longitudinal observations and most often use patient global ratings of change as criterion. This way, the difference in observed score changes between those who experience worsening versus those without worsening (according to patient global ratings of change) can aid to estimate of clinically relevant change: score changes outside the 95% confidence limits of those observed in the stable group are used as one of several defintions of MID. If patient global ratings of change are dichotomized, receiver operating characteristics can be applied to determine the amount of change in clinical rating scale score that would most accurately classify the sample according to patient ratings. In contrast, the SDC is related to the standard error of measurement (SEM) and can be understood as a cutoff, above which score changes can be confidentially considered above measurement error. The SDC can be defined from score variance in re-test in subjects assumed to be stable over time (e.g., according to patient global ratings of change) in samples of appropriate size. If MID or SDC is reported for clinical rating scales, it is usually provided for the total score only and responsiveness may differ at item level (O’Connor et  al. 2004). SDC for intra-­ individual observation is higher, while SDC for group observations can be derived as individual SDC divided by √n of sample. If SDC were determined appropriately, score changes below SDC should strictly not be considered meaningful. With respect to MID, however, several reviews pointed out that MID should not be applied too rigidly, as it depends on the type of anchor used, may depend on context of use, baseline value, and also on direction of change (O’Connor et al. 2004; de Vet et al. 2006; Norman et al. 2003; Revicki et al. 2008). When selecting a clinical rating scale for a given sample, one should carefully check the appropriateness of a given scale beyond the quality criteria mentioned above: has it been applied in the specific disease before? Are there major differences in patient characteristics between validation samples and intended use? Have floor or ceiling effects been observed in relevant proportions? Does the content of the scale cover the symptoms or functions of interest? A recent review on clinical rating scales for ataxia (Perez-­ Lloret et al. 2021) comprehensively listed the quality criteria along with past and current use in validation studies and clinical trials. Indeed, comparability with results from previous or competing trials may be a consideration but should not

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neglect evidence on quality and specific acceptability of an outcome. At last, status of licensing should be checked before using an instrument in clinical trials as well as time needed to perform the test and appropriate rater training. While there is consensus that patient reported outcomes need application in valid translations, there is less consensus inhowfar translations of clinical rating scales would be useful or how this might affect their metric properties. If translated versions are used, these should undergo at least procedures for linguistic validation.

3 ICARS—The International Cooperative Ataxia Rating Scale The scale was devised by an expert ad hoc committee in 1997 as a first international standard for the clinical rating of ataxia (Trouillas et al. 1997). Authors acknowledged that cerebellar signs may occur within a more complex syndrome in ataxia disorders, but selected items according to their assumed specificity for ataxia. The 19 items cover four domains of body functioning which were proposed as subscales of the total score: posture/gait (seven items, maximum 34 points), kinetic functions, rated on both sides (seven items, maximum 52 points), speech function (two items, maximum eight points), and oculomotor function (three items, maximum six points). The total score is built by addition of subscores and describes severity of ataxia (0 = no ataxia; 100 = most severe ataxia). Although the original publication did not contain data on validation, the scale gained wide use for the clinical description of ataxia populations and several studies evaluated its metric properties. Results generally support reliability between raters or at retest for the total score in different ataxias, but also revealed limitations and the need for rater training. Scoring instructions were noted as imprecise for some kinetic items and interdependent ratings were noted for posture/gait items (SchmitzHubsch et al. 2006), Parkinsonian features seemed to contaminate ataxia rating in MSA (Tison et al. 2002), and re-test reliability was lower for speech and oculomotor items. Sufficient internal consistency was reported from samples of MSA, spinocerebellar ataxias(SCA), FRDA (Bürk et al. 2009) Multiple Sclerosis, and focal cerebellar lesions (Schoch et al. 2007). Factorial analysis did not fully support the four domains of the scale in most studies, such that use of subscales was not endorsed except for MSA and focal cerebellar lesions. Validity was shown against different clinical rating scales, disease stages or measures of activity limitations in FRDA, SCA, MSA, multiple sclerosis with ataxic symptoms (Salci et  al. 2017), ataxia teleangiectasia (Nissenkorn et  al. 2016), and FXTAS. The longitudinal assessment of disease progression in FRDA reported a 5-point change at 12 months, with corresponding ES of 0.26 and SRM of 0.74 (Fahey et al. 2007). Different progression rates for different disability stages and a plateau in ICARS ratings of later stage FRDA may point to non-linear properties of the scale but may also be interpreted as inherent characteristics of the disease (Tai et al. 2015a; Ribai et al. 2007).

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Further exploration of linearity, that is, discriminant abilities over the whole range of scoring, has not been reported. In common with most clinical rating scales, benchmarks of smallest detectable difference or indicators of patient relevance of score changes have not been established for ICARS.

4 UMSARS—The Unified Multiple Systems Atrophy Rating Scale This scale was developed and published in 2004 by the Multiple System Atrophy Study Group (Wenning et al. 2004). The development was triggered by the prospect of clinical trials for neuroprotective interventions and the need for a valid and reliable outcome to prove treatment efficacy in MSA. This neurodegenerative disorder clinically combines features of ataxia, Parkinsonism, pyramidal signs, and autonomic failure which are all considered in a comprehensive rating. Similar to the Unified Parkinson’s Disease Rating Scale, this rating instrument was devised as a multi-dimensional scale and is structured in four parts. Part I (12 items, maximum 48 points) is rated by interview to assess impairment of bodily functions or limitations in daily activities over the past 2 weeks, regardless of the nature of the signs. Part II rates the results of a motor examination (14 items, maximum 56 points) with specific attention to features of ataxia and Parkinsonism. Of note, limb items are tested on both sides but only worse side is included in rating. Part III reports results of a standard bedside tests of orthostatic dysregulation without rating, while part IV consists of a 5-step disability rating (see below). The original publication reported high reliability for parts I and II according to Cronbach’s alpha, but item-total correlations suggested some inconsistency with total scores for part I items 8 (falling) and 9 (orthostatic symptoms) and part II item 3 (oculomotor dysfunction). Inter-­ rater agreement was high to excellent (ICC >0.85) for the subscores, but lower at item-level for ratings of oculomotor dysfunction, muscle tone, rapid alternating movements, and finger tapping. Validity of both, part I and part II, was supported by comparison against a 3-step global disability rating. Test-retest reliability was determined as high in independent samples (Krismer et al. 2012). Subsequent use of the scale in longitudinal observational studies in a European MSA cohort reported part I/part II score changes of 6.7/9.6 at 12 months (Geser et al. 2006). Faster progression according to UMSARS in those with milder disability and shorter disease duration may be considered a feature of the disease but may also point to non-linearity of the rating. Longitudinal observation in a US sample reported lower progression rates of 3.1/4.5 in part I/part II after 12 months (May et al. 2007). The scale has also been applied in spinocerebellar ataxia (SCA) type 3 (D’Abreu et al. 2007) in which a 2.7 point worsening (part II) was observed over 13 months. In both, MSA and SCA3, correlations between UMSARS part II and ICARS ratings were high and support shared constructs. While benchmarks for reliable change (SDC) have not been reported for this scale, one study defined MID for symptom worsening in MSA of Parkinsonian type

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using receiver operating characteristics with clinical global impression of change as an anchor (Krismer et al. 2016). However, the adequacy of reported MID as 1.5/1.5 points for part I and part II, respectively, in MSA of cerebellar type remains to be shown.

5 FARS—The Friedreich Ataxia Rating Scale This scale was developed by the North American Cooperative Ataxia Group and published in 2005 (Subramony et  al. 2005). Developers of the scale aimed for a valid and potentially responsive clinical assessment in Friedreich’s ataxia (FRDA). As a starting point, they acknowledge the symptom spectrum specific to this disorder and the intraindividual evolution of symptoms over time, such that clinical findings seen in earlier stages (e.g., nystagmus) may become absent at later stages despite progression of disease. They chose to assign highest weight to the assessment of gait and stance. The scale is multi-dimensional and comprises four parts. Part I consists of a 6-step functional staging of ataxia (see below). Part II targets restrictions in ADL performance in 9 items (maximum score 36), while part III provides clinical rating from a standardized motor examination (22 items, maximum score 117, limb items rated each on both sides). Scale structure suggests five subscores within part III: bulbar (maximum score 11), upper extremity (36), lower extremity (16), peripheral (26), and upright stability (28/36 in 2006 FARSn revision (Lynch et al. 2006)). Part IV comprises quantitative stopwatch tests of speech, hand function, and gait capacity. The original publication reported excellent inter-rater reliability for most scores except for part III bulbar and peripheral nerve. Correlations among the scale’s parts were substantial, even in the small cohort of 14 patients, with the exception of part III bulbar. In some studies, FARS is reported as sum of parts I to III (maximum score 159). Factor analysis from independent samples did not fully support the scale structure and part III subscores (Rummey et al. 2019). A modified version of the FARS part III—mFARS—has been proposed which excluded two bulbar items (facial and tongue atrophy) and the peripheral items (maximum score 93). This mFARS has seen increasing and preferential use over FARS part III in observational and interventional trials (Reetz et al. 2021; Xiong et al. 2020; Lynch et al. 2021). A recent cross-sectional validation of both, original and modified mFARS in a large multi-national cohort, confirmed improved construct validity (Rummey et  al. 2019). Of note, modification removes the non-­ cerebellar items of the original scale (Fig. 2). Reports on clinical validation in samples of more than 50 patients established correlations >0.9 of FARS part III with ICARS (Fahey et al. 2007), SARA (Bürk et  al. 2009), and measures of functional disability and activities of daily living (Lynch et al. 2006). When analyzed by subscores of FARS part III, such correlations remained high only for the upright stability subscore. Data on responsiveness from longitudinal observation in larger samples (Regner et  al. 2012; Friedman et  al. 2010) showed convergent decline in parts I to III of the FARS. For part III, this

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Fig. 2  Illustration of the proportional representations as percentage contribution to total score per different domains affected by ataxias for the rating scales reported herein: two patient-reported outcomes (PRO Ataxia and FAIS, left) and clinical ataxia rating scales. Other functions refer to non-ataxia signs or functions or functions not unequivocally attributable

change of 3.6/6.2 points after 1/2 years corresponds to standardized response mean (SRM) of 0.53 at 12  months and 0.84 after 2 years. This effect seemed mainly driven by limb coordination and upright stability subscores. Importantly, this study suggested ceiling effects of the scale in patients with higher disability. Moderate correlations were established between FARS part II/III and the physical component summary of the Short-Form Health Survey 36 (SF36) (Tai et al. 2017a). Benchmarks of SDC or MID have not been formally established for FARS or mFARS, but valuable evidence can be drawn from recent RCTs which described mFARS reductions of up to −2.3 in placebo groups after 3 months observation that seems to vane thereafter (Lynch et al. 2019a, b). Based on this, a recent study used mFARS score change of ≤−1.9 as criterion for clinical improvement and reported mean mFARS reduction of −1.55 with omaveloxolone compared to mean increase of 0.85 in placebo at 48 weeks (Lynch et al. 2021).

6 SARA—Scale for the Assessment and Rating of Ataxia Published in 2006, this scale was devised by an European consortium to address the need for a validated assessment to evaluate therapeutic interventions in spinocerebellar ataxias (Schmitz-Hübsch et  al. 2006). Existing scales were not considered suitable for this purpose due to concerns on practicability and construct validity (ICARS) or disease-specific design (FARS). Therefore, SARA was designed as a

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generic measure to assess cerebellar ataxia on an impairment level, that is, the level of physical functions. Item selection and rating instructions were by expert consensus and aimed for specificity for ataxia, standardized instructions, and coverage of the full range of symptom severity over the disease course. Further, the contributions of ratings in gait/stance, speech, and limb items to total score were chosen to reflect their impact on patient functioning. Of note, limb items are rated separately for each side, but only the means of both sides are included in total score. The assessment of oculomotor functions was not included in the final version of the scale, because results of a first validation trial were not supportive. The original report contained results of scale validation in 119 subjects with spinocerebellar ataxias and 110 controls. No floor or ceiling effects were observed and all patients scored ≥1.5 on SARA total. The observation of positive ratings in 21% of controls was mainly in limb kinetic functions of the non-dominant side. Factor analysis supported the unidimensional structure of the scale. Reliability was high for the total score both between raters and at retest. At item-level, inter-rater reliability was lower (but ICC still >0.7) for items 6 (finger-nose test) and item 8 (heel-shin test) on the left side. Regression analysis were performed against global clinical ratings of ataxia severity performed on video recordings in a subset. Results supported linearity of ratings over the whole range of the scale and also linearity of score differences. Validity was shown by high convergence to ataxia disease stages (see below) as well as measures of activity limitations. The scale has gained wide acceptance in clinical use due its practicality. Subsequent evaluations of its metric properties generally confirmed high reliability, internal consistency, and convergent validity in a heterogeneous ataxia population (Weyer et al. 2007). In patients with Friedreich’s ataxia, high correlations of r > 0.9 were seen with ratings on ICARS and FARS (Bürk et al. 2009). Data are also available from application in different SCA, FRDA (Marelli et al. 2012), and rarer recessive ataxias (Nissenkorn et  al. 2016; Traschütz et  al. 2020, 2021; Bourcier et  al. 2020), Niemann-Pick type C (Patterson et al. 2021), multiple sclerosis with ataxic symptoms (Salci et al. 2017), cerebellar stroke (Choi et al. 2018; Kim et al. 2011), and pediatric brain tumors (Hartley et  al. 2015). Importantly, use in early onset ataxia explored the utility in children (Lawerman et al. 2016). While inter-rater reliability was high, limitations in validity were noted due to coincident non-cerebellar manifestation and motor development (Brandsma et al. 2017; Lawerman et al. 2017). Data on scale’s responsiveness are available from several long-term observational studies in SCA1, 2, 3, and 6 (Schmitz-Hübsch et al. 2010a; Jacobi et al. 2011, 2015; Ashizawa et al. 2013). For the mixed sample, SARA changes at 12 months were reported per group of patients with worsening according to patient global impression as increase of 1.69  ±  2.9 points (95% limits of confidence 1.2–2.2), which corresponds to a SRM of 0.59, while in patients who did not report worsening, SARA change was as minimal as 0.43 ± 2.1-point increase (95% confidence interval [CI] −0.2–1.1, SRM: 0.21). Authors suggested the upper limits of change in subjects without worsening, that is, a 1.2 increase in SARA, as a benchmark for minimal clinically important change and SARA change at 12  months classified those with subjective worsening with sufficient accuracy (area under the curve

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[AUC]: 0.613) (Schmitz-Hübsch et  al. 2010a). Natural history studies of SARA have been summarized in a recent meta-analysis comprising >1200 patients and reported striking similarity of findings from European, Asian, and US cohorts. Annual pooled SARA score increase was highest in SCA1 with 1.83 (1.46–2.20), intermediate in SCA2 with 1.40 (1.19–1.61) and SCA3 with 1.41 (0.97–1.84), and lowest in SCA6 with 0.81 (0.66–0.97). For patients with SCA3, disease progression was faster in studies located in Asia and Europe than in the US. Progression rates have also been reported from longitudinal analysis in SCA7 (Contreras et al. 2021; Marianelli et al. 2021), CANVAS (Traschütz et al. 2021), COQ8A ataxia (Traschütz et al. 2020), ARSACS (Bourcier et al. 2020), and FRDA (Reetz et al. 2016, 2021; Marelli et  al. 2012). Of note, the study in ARSACS included a Delphi survey to confirm content validity. Smallest detectable change was determined as 3.5-point change on individual SARA scores by distribution-based methods in a mixed SCA sample (Schmitz-Hübsch et  al. 2010a) and replication in an ARSACS sample yielded similar results (SDC 3.06). A recent study reported responsiveness to clinical improvement also in autoimmune ataxia (Damato et  al. 2021). Again, useful information may be drawn from recent or future use in interventional trials. However, for the compounds studied with SARA as the clinical outcome, mean SARA score changes did not exceed SDC in neither treatment nor placebo groups, and no symptomatic effect could be established to date (Feil et al. 2021; Romano et al. 2015; Coarelli et al. 2022).

7 NESSCA—Neurological Examination Score for Spinocerebellar Ataxia This instrument has been used by developers since 2001 and metric properties were published in 2008 (Kieling et al. 2008). The scale is designed to comprehensively capture signs of spinocerebellar ataxias, focused on SCA3, which comprises pathology of cerebellum and additional involvement of other structures. Thirteen out of 18 items cover clinical signs of ataxia (gait, limb speech, oculomotor dysfunctions) as well as pyramidal and extrapyramidal affection, lower motoneuron, and peripheral nerves. The remaining five items cover patient report on dysphagia, vertigo, sphincter function, and cramps. Total score ranges from 0 (no affection) to 40 (maximum severity). Factorial analysis supported a multi-dimensional structure of the scale. Validation data are available from the SCA3 and SCA2 cohorts, which reported sufficient internal consistency, high inter-rater reliability, while re-test reliability was not investigated. Convergent validity was demonstrated in SCA3 by correlation with disease stages (rho > 0.75). Associations with SARA ratings were high in a subgroup of the cohort (r  >  0.85) which also applied for NESSCA subscores of ataxia (r = 0.84) and non-ataxia (r = 0.76) items. In a separate study, correlations with SARA were lower in SCA2 (r > 0.6) (Monte et al. 2017, 2018). Responsiveness of 1.26 point change at 12  months has been reported from a large observational

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study in SCA3 (Jardim et al. 2010). However, test-retest reliability and thus standard error of measurement as well a minimal clinically important change have not been determined.

8 BARS—The Brief Ataxia Rating Scale This scale—published in 2009—was developed as a derivative of a modified version of ICARS with the intention to increase practicality for clinical use (Schmahmann et al. 2009). Item selection was based on the evaluation of item-total correlations for the modified ICARS, but also integrated expert decision to incorporate at least one item per domains of gait, kinetic functions arm, kinetic functions leg and, speech, and eye movements. The scale consists of only five items yielding a maximum score of 30 which by design is highly correlated with ICARS and modified ICARS. According to the original publication, application in a separate cohort demonstrated high reliability (internal consistency and inter-rater reliability) and criterion validity against ICARS. Further use specifically supported practicality in children, in which high inter-rater and retest reliability were confirmed (Brandsma et al. 2014). Convergent validity was established in patients with ataxia teleangiextasia (Nissenkorn et al. 2016) and children with posterior fossa brain tumors (Hartley et  al. 2015). However, no report is available from longitudinal observation and benchmarks for reliable and important change remain to be determined.

9 CCAS Scale—Cerebellar Cognitive Affective Syndrome Scale This scale’s content covers a different construct: whereas the clinical rating scales based on neurological examination target different motor functions, the CCAS targets cognitive-affective sequalae of cerebellar dysfunction that may add to the limitations and restrictions perceived by patients with ataxia. The scale was published in 2018 and developed as an office and bedside screening test (Hoche et al. 2018). The development was guided by previous description of the CCAS (Schmahmann and Sherman 1998) characterized by deficits in executive function, linguistic processing, spatial cognition, and affect regulation. The CCAS Scale comprises short standardized tests of nine cognitive functions and one composite item on affective disturbance based on clinical judgement. Cognitive tests cover semantic and phonemic fluency, category switching, verbal learning, digit span forward and backward, cube drawing, similarities, and go no-go task. Scoring instructions yield raw scores per item to form a sum score (0 = fail, 120 = highest performance). For use as a screening instrument, however, authors propose fail/pass criteria based on normative data for each item and define possible, probable, and definite CCAS if subjects

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fail in one, two, or three items, respectively. Validity has been established as sensitivity and selectivity in an ataxia group of mixed etiology and pathology in comparison to controls. The original publication provided three alternative versions for repeated testing but did not provide guidance on acceptable intervals for retest. The scale has since been used in SCA2 (Rodríguez-Labrada et al. 2021), SCA3 (Maas et  al. 2021b), ataxia teleangiectasia (Hoche et  al. 2019), and cerebellar stroke (Chirino-Pérez et  al. 2021) and supported prevalence of CCAS, confirmed high sensitivity of CCAS scale but revealed also limited specificity for single items and an influence of ataxia severity, age, and education on scale performance. The need for transcultural adaptation is under investigation (Thieme et al. 2020). Reliability and responsiveness of the scale remains to be shown in the target group of adult persons with ataxia as well as applicability and validity in children.

10 INAS—Inventory of Non-Ataxia Symptoms This inventory was originally devised for comprehensive phenotyping and descriptive analysis in a mixed SCA cohort, in which the occurrence of non-cerebellar features had to be expected (Schmitz-Hübsch et al. 2008a). The feature content was selected by expert consensus and respective features listed for documentation by the rater as none, mild, moderate, or severe. Of note, no further instructions on testing or 4-step item-level ratings are provided. In later analyses, items were grouped according to 16 systems and presence of a sign of any severity within a group led to assignment of 1 for that group. The resulting INAS count represents the sum of affected systems and provides a rough measure of the extent of non-cerebellar affection in a single patient or study population (Jacobi et al. 2013a). An 0.37 increase of INAS count was observed at 12 months in a mixed sample of SCA1, 2, 3, and 6, but the standardized response mean was much lower (0.26) than for SARA score changes.

11 Disability Staging Disability staging refers to a more condensed rating of overall performance or restrictions. It is not based on standard testing but rather a clinical judgement of severity with some anchor provided in scale description. Stagings are not designed to capture changes in disease status in the shorter term, but have a role as an external validation criterion in the development of clinical rating scales or may be used as a classification to stratify patient samples. The widely used 4-step ataxia disease stages were first applied in a large retrospective analysis of the natural history of ataxias (Klockgether et al. 1998). A different staging with range from 0 (no cerebellar sign) to 7 (bedridden) has been proposed as spinocerebellar degeneration functional score (SDFS) and was applied in recessive ataxias and MSA (Anheim et al. 2010;

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Wirth et al. 2022). Different stagings also form part of the FARS (part I, 7-step rating) and UMSARS (part IV, 5-step rating). While ataxia disease stages and FARS staging only rely on limitations of mobility, the UMSARS part IV is more comprehensive to include any limitation.

11.1 Mobility Stages (Klockgether et al. 1998) Stage 0 No gait difficulties. Stage 1 Disease onset, as defined by onset of gait difficulties. Stage 2 Loss of independent gait, as defined by permanent use of a walking aid or reliance on a supporting arm. Stage 3 Confinement to wheelchair, as defined by permanent use of a wheelchair.

11.2 UMSARS Part IV: Global Disability Scale (Wenning et al. 2004) Score 1 Completely independent. Able to do all chores with minimal difficulty or impairment. Essentially normal. Unaware of any difficulty. Score 2 Not completely independent. Needs help with some chores. Score 3 More dependent. Help with half of chores. Spends a large part of the day with chores. Score 4 Very dependent. Now and then does a few chores alone or begins alone. Much help needed. Score 5 Totally dependent and helpless. Bedridden.

11.3 FARS—Functional Staging of Ataxia (Subramony et al. 2005) Increment by 0.5 may be used if the status is about the middle between two stages. Stage 0 Stage 1.0 Stage 2.0 Stage 3.0

Normal. Minimal signs detected by physician during screening. Can run or jump without loss of balance. No disability. Symptoms present, recognized by patient, but still mild. Cannot run or jump without losing balance. The patient is physically capable of leading an independent life, but daily activities may be somewhat restricted. Minimal disability. Symptoms are overt and significant. Requires regular or periodic holding onto wall/ furniture or use of a cane for stability and walking. Mild disability. (Note: many patients postpone obtaining a cane by avoiding open spaces and walking with the aid of walls/people etc. These patients are grades as stage 3.0)

330 Stage 4.0 Stage 5.0 Stage 6.0

T. Schmitz-Hübsch Walking requires a walker, Canadian crutches or two canes. Or other aids such as walking dogs. Can perform several activities of daily living. Moderate disability. Confined but can navigate a wheelchair. Can perform some activities of daily living that do not require standing or walking. Severe disability. Confined to wheelchair or bed with total dependency for all activities of daily living. Total disability.

12 Remote Assessment of Ataxia Technical developments and promotion of telehealth as well as clinical need have fostered the exploration of remote assessment of ataxia. In the most simple form, patients are instructed by an application on their mobile device to record videos during performance of a set of standard motor tasks. These videos can then be transferred and rated offline by a clinical rater. This approach has been published for a five-item adaptation of SARA, the SARAhome, which includes gait, stance, speech, nose-finger test, and fast alternating hand movements (range: 0–28) (Grobe-Einsler et  al. 2021). The first application demonstrated near-perfect correlations of SARAhome ratings to full SARA scores obtained at in-patient visits. The study yielded important information on the applicability of multi-point testing in the home setting and revealed considerable intra-individual variability. Variability of SARAhome recordings performed remotely once per day for a period of 14 days amounted to SARAhome differences between 1 and 5.5/28 points. This finding may be generalizable to other similar approaches of remote assessment and needs consideration when integrating remote functional assessments in trial design. A recent pilot study in FRDA patients demonstrated the feasibility to perform video recording of modified FARS and full SARA at home with assistance of a caregiver. Reliability at repeated testing was high for SARA, FARS III, and its subscores upright stability and lower limbs (Tai et al. 2021). As an extension of conventional videorating, consumer grade infrared cameras have been explored for their utility to remotely assess ataxic children. Recordings used the conventional hardware setup of the Microsoft Kinect V2.0 RGB-depth camera and a customized user interface and analysis software. The pilot trial reported applicability and acceptance as promising without further detail on rating (Summa et al. 2020a). The test protocol was inspired by SARA items and therefore named SaraHome (Summa et al. 2020b). Of note, SaraHome denotes a testing protocol distinct from SARAhome described above, despite similar names. From the perspective of outcome metrics, the use of remote assessment in clinical trials will need to assure smooth patient experience and safety, counteract attrition in use, ensure sufficient data quality, that is, establish the accuracy and reliability in unsupervised application.

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13 Functional Composite Scores Scores from clinical rating scales are inherently semi-quantitative and at best interval ratings. Therefore, simple or more elaborate performance tests have been introduced as quantitative performance measures. This was expected to yield higher sensitivity to change and improve reproducibility by elimination of rater interpretation. Different combinations of simple stopwatch tests have been explored in ataxias, based on the observation that motor incoordination results in slowing of motor performance and drawing from experience with the Functional Composite in Multiple Sclerosis. As a general drawback, achieving scores from raw data (i.e., transformation of performance times into Z-scores or other Indices) is not straightforward (e.g., decision on choice of reference population to calculate Z scores) and interpretation of change is often unresolved. Further, inability to perform a test at follow-up needs to be coded as informative missing and use is generally limited by patient’s capacity to perform individual tests. Normative data should be available to estimate effects of age for interpretation. Multiple composite scores of such timed tests has been devised and explored for ataxias. Of note, results from the composite functional score described below are often reported per test component and components have also been singled out as an outcome, such as 8 m-walk (8 MW) or timed 25-ft walking test (T25FW) for walking function, 9-hole peg test (9HPT) or click test for hand function, and syllable repetition (PATA) for speech function. The composite FARS part IV consists of three performance tests: PATA rate (speech function), time to perform the 9-hole peg test (hand dexterity function), and timed 25-foot walk test (gait function) (Subramony et al. 2005). A similar composite, the AFCS (Ataxia Functional Composite Scale), also includes low contrast visual acuity test (visual function) (Assadi et al. 2008; Lynch et al. 2005). It has been applied in SCA and FRDA with high retest reliability and strong correlation to clinical severity ratings (Lynch et al. 2006). Responsiveness in FRDA patients was reported for timeframes up to 3 years and seemed highest for the 9HPT component (Friedman et al. 2010; Tai et al. 2017b). The CCFS (Composite Cerebellar Functional Severity Score) consists of two tests of hand coordination: 9-hole peg test and click test (du Montcel et al. 2008). Performance times are transformed into age-corrected scores. Validation reported high retest reliability and convergent validity with SARA scores in SCA patients (du Montcel et al. 2008) and FRDA (Tanguy Melac et al. 2018). Responsiveness was reported as a score change at 12  months with SRM >0.6  in SCA1, 2 and 3 (Chan et al. 2011) but no minimally important change was reported. Of note, CCFS differed between mutation carriers and non-carriers in the pre-manifest phase for SCA1, 2, and 3 (Jacobi et al. 2013b). The SCAFI (SCA Functional index) was devised for use in SCA (Schmitz-­ Hübsch et al. 2008b). It consists of similar tests as FARS part IV: PATA rate, 9 hole peg test, and 8 m walk at maximum speed. It uses standardized computation of Z-scores per test that also integrates codings for inability to perform.

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Test-retest reliability was good in SCA and convergent validity to clinical ataxia ratings was shown in SCA1, 2, 3, and 6 (Schmitz-Hübsch et  al. 2010a) and FRDA (Reetz et al. 2015). Application in at-risk cohorts suggested sensitivity in the pre-symptomatic phase of SCA2 (Jacobi et  al. 2013b). Responsiveness of SCAFI was shown for a mixed cohort of SCA1, 2, 3, and 6 at 12 months, but not for its component 8  m walk or PATA.  Most favorable results were seen for 9-hole peg test with SRM of 0.67, that is, superior to change in SARA scores (SRM of 0.5 in the same sample). However, 9HPT changes did not discriminate between groups with and without perceived worsening according to patient global ratings of change (Schmitz-Hübsch et al. 2010a). This may be interpreted to reflect higher sensitivity of this test to detect “sub-clinical” change, but as a consequence leaves importance or relevance of change to patients an open issue. In clinical trials, this should be accounted for by multi-dimensional assessment with complementary assessment at the level of patient perception. As a generic measure, the NIH Toolbox for the Assessment of Neurological and Behavioral Function was proposed as a flexible assessment battery for use in neurological disorders (Gershon et  al. 2010a). It contains a set of tests for dexterity, strength, balance, locomotion, and endurance that have undergone thorough validation and are provided along with recently assembled normative datasets over an age spectrum of 3–82 years. To date, no published study in ataxias referenced the NIH toolbox.

14 Instrumented Motor Testing Impaired motor performance is well amenable not only to observer ratings but also to technical recording, which, if applied along with a standard test instruction, may be referred to as instrumented motor testing. Such recordings may yield quantitative descriptors of movement for clinical use, recently referred to as “motor biomarkers.” A multitude of technical advancements (e.g., force plates, wearable inertial sensors, marker-based optical systems, and marker-free visual perceptive computing) have been used for this purpose and most often apply kinematic analyses. Though evidence is still scattered by virtue of different technologies, algorithms, and standardization of tasks, some convergent findings can be subsumed. Beyond slowing of movement—that may be detected by timed tests described above—further clinical features of ataxia may be quantified such as broadened step width while walking or increased trunk movements while standing or walking. In a review of instrumented motor testing of limb coordination, authors proposed a set of affected domains of motor performance which may help to delineate the specific pathology across different studies, devices, and metrics (Power et al. 2021). Increased variability of movement has been described in spatial and temporal domains, most often reported for locomotor stepping during walking tasks, but also for speech function (Ilg et  al. 2012; Kroneberg et  al. 2018; Shah et  al. 2021; Schniepp et  al. 2014;

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Rochester et al. 2014; Vogel et al. 2020; Hickey et al. 2016; Schmitz-Hubsch et al. 2016). Intriguingly, evidence converges that subtle changes in motor biomarkers such as variability can be detected in pre-ataxic carriers, that is, persons without symptoms or clinical ratings of ataxia (Rochester et  al. 2014; Vogel et  al. 2020; Thierfelder et al. 2022; Ilg et al. 2016; Velázquez-Pérez et al. 2021). Another advantage of such technology is its potential for rater-independent remote recording (see above). Unobtrusive instrumentation may even extend beyond task-based assessments to the recording of real-life activities. While commercial activity monitors are usually confined to types and amount of physical activity or step count per day (Schniepp et  al. 2022), recent research developed promising approaches for quantitative gait analysis from real-life walking (Thierfelder et al. 2022; Ilg et al. 2020; Shema-Shiratzky et al. 2020). Compared to the wealth of literature in this field, only few studies systematically explored the metric properties, validity, and responsiveness of such measures (Milne et al. 2018, 2021) and they are not (yet) part of the protocols of the large natural history studies in ataxias.

15 Patient-Reported Outcomes for Ataxias Patient-reported outcomes (PRO) refer to the assessment of patient experience, most often by self-report questionnaires, but may also be obtained by structured interview or even from caregivers. By content, PRO cover those features of disease that are amenable to patient perception, such as disturbance of motor coordination, weakness, or numbness. Importantly, for some of the features, patients only can validly report on presence or absence or severity of a specific symptom. This typically applies to the presence or the severity of pain, mood, fatigue, impairment in executive functions or behavioral change. Further, patient report is important to capture aspects of disease that may not become evident at the clinical visit. This applies to episodic phenomena such as seizures, falls, or infrequent disturbance (myoclonus, spasms) but also to disturbed sleep or other autonomous dysfunctions. Not least, patient report is essential to evaluate the impact of disease on everyday functioning and general well-being, which are conceived as the target of all medical procedures, according to the current positive definition of health endorsed by the World Health Organization (WHO). In this sense, for chronic diseases, patient-­ report of functioning, well-being, and life satisfaction is a valid anchor to establish the relevance of change in other outcome assessments or biomarkers or gauge the effectiveness of medical interventions. Consequently, regulatory bodies have emphasized that patient view should be considered in study planning and generally recommend inclusion of PRO as one of the study outcomes. The term PRO subsumes a variety of instruments that can largely differ in construct and dimensionality. As a guidance, one first important distinction is whether patient report is used to identify and scale specific symptoms or combinations thereof, such as pain or depression questionnaires. Most of these instruments are specific to the symptom but not confined to use in specific diseases. Still, their

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content should be checked for applicability in the population under study with respect to their disability levels. Other PRO address the impact of disease and treatments on patient performance in activities of daily living (ADL), perceived (health-related) quality of life (hrQOL), and general well-being. All three constructs are generally conceived as somehow related to symptom severity—whether assessed with PRO or clinical rating scales— but as different in several aspects. First, they are notably subject to many other factors than disease such as role perceptions, coping, treatment settings, social and family support, lifestyle, and occupational status. Specifically for the construct of hrQOL, the WHO definition explicitly states the specific cultural context as the reference for self-perception and well-being. This makes such instruments prone to some cultural or lifestyle bias that need consideration when used in contexts different from those in which the PRO was developed. Second, PRO of ADL or hrQOL usually have a multi-dimensional structure designed to assess all but only relevant domains of the disease and users should check the applicability of the content in their target group. For example, generic hrQOL instruments usually consider the domains of physical functioning, mood, ADL, and social roles as known health-­ related determinants of subjective well-being and life satisfaction. For disease-­ specific PRO of ADL or hrQOL, the development usually integrates both knowledge on domains affected by the specific disease and knowledge of their relevance to those affected. It is inevitable, that questions on hrQOL relate to the severity of symptoms to some extent, asking for example “how much did your problem in motor coordination prevent you from …?”. All this implies that the ADL type of PRO should be considered as more specific to a disease compared to generic hrQOL PROs and explains usually moderate relations to disease severity at the level of symptoms or structural level. Using a generic PRO for ADL or hrQOL can be a good choice to enable comparison between ataxias or across medical conditions or with population level data. However, PRO developed for use in specific diseases may be perceived as more adequate by the patients and are expected to offer better sensitivity to change over time. Of note, the self-rating of walking ability stands somewhere in between the symptom-specific PRO and the PRO of ADL, as disturbance of gait can be considered both as a clinical sign (and assessed in clinical rating) but also as an impairment of a fundamental domain of everyday functioning, related to physical activity levels, general mobility, social participation, and thereby impacting on hrQOL. Generally, the move towards patient-centered research and cost-effectiveness research has led to increasing numbers of PRO instruments for different disorders, but also increasing standards for their development and use in clinical trials. Not least, multi-national trials will need at least linguistically validated translations when applying the same PRO in different countries. Further, practical applicability of paper-pencil or computerized PRO versions may need adaptations according to the levels of hand function impairment. Likewise, applicability in pediatric populations or those with cognitive impairment need consideration. To deal with the increasing demands on PRO, also from a regulatory perspective, overarching efforts have been put into operation to develop a framework for generalizable item banks

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or common metrics, specifically for neurological disorders. One such example is the US National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS) Roadmap initiative (www.nihpromis.org) (Gershon et  al. 2010b). This initiative aims to provide a set of psychometrically validated PRO for several constructs of clinical relevance, that can be flexibly combined to “evaluate and monitor physical, mental, and social health in adults and children and can be used with the general population and with individuals living with chronic conditions” (www.healthmeasures.net). From the same site, a set of PRO (Neuro-­ QoL) is available selected for specific relevance for neurological disorders. The following sections will shortly describe the most commonly used generic PRO of hrQOL and also describe the few PRO specific for ataxias and will mention the PRO of ADL that have been reported in large ataxia cohorts.

15.1 Generic PRO of hrQOL The EQ-5D and the SF36 have most frequently been applied as generic instruments in ataxia studies including large observational trials (Tai et al. 2017a; Bolzan et al. 2021; Wilson et al. 2007). Although shown to reflect relevant change in impact of disease, low repeatability of EQ-VAS and low effect sizes in longitudinal observation, for example, preclude use as proxy for disease progression in longitudinal or interventional studies (Schmitz-Hübsch et al. 2010a, b; Jacobi et al. 2018). For the evolution of SF36 in FRDA, independent observations reported decline limited to SF36 physical and role limitation subscores with stable mental subscores (despite worsening in clinical ratings) (Xiong et al. 2020; Tai et al. 2017a). Improved metric properties may be expected for the more recently devised PROMIS V1.2 general health instrument (Hays et al. 2009). It consists of 10 questions on general well-being, quality of life, bodily functioning, psychological functioning, life satisfaction, activities, social roles, mood, fatigue, and pain. While pain is rated on a 0–10 numeric scale, the remaining items are rated on five-step Likert rating. To date, no data are available on use of any PROMIS or NeurQol instrument in ataxias. Apart from instruments that target hrQOL or activity limitations, distinct functions or symptoms may be assessed using specific questionnaires, such as balance confidence ratings (Powell and Myers 1995), questionnaire on walking function (Brogardh et al. 2021), fatigue (FSS (Krupp et al. 1989) or FSMC (Penner et al. 2009)), or instruments for sleep quality, mood, pain, or autonomic dysfunctions. Of note, some of these functions are also covered in the more recent NeuroQol but will need linguistically valid translations for international use.

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15.2 PRO Specific for Ataxias Patient-report of disease symptoms and their impact form part of the disease-­specific clinical ataxia rating scales FARS (part II) and UMSARS (part II). Further, the ADL part of the Unified Rating Scale for Huntington’s Disease (UHDRS part IV) has been used in large longitudinal trials in adult-onset ataxias. However, in all three scales assessment is often rater-based and obtained by structured interview and scoring thus involves some interpretation by the rater. For FARS part II, use as self-­ report questionnaire is preferentially applied in US sites which may lead to site differences in outcome unrelated to the disease (Reetz et al. 2021). Thus, type of assessment should be specified in the study protocols and reported along with study results.

15.3 FAIS—Friedreich’s Ataxia Impact Scale This first disease-specific PROM for application as a patient questionnaire was published in 2009 (Cano et al. 2009). It was conceptualized to assess the health impact of FRDA in clinical studies. Development of FAIS followed current methodology of scale construction including qualitative research and patient involvement to generate a conceptual framework and first item pool and Rasch measurement methods for item selection. The questionnaire consists of a 126-item long form, divided into eight subscales (speech, body movement, upper limb, complex tasks, self-­perception, isolation—each with three response options—and lower limb and mood rated with four response options). As all items are scaled to a common construct, subsets of items may be selected for short forms that may better apply to specific populations, for example, more or less impaired, as proposed in the original work. Although rigorously designed, the stability of response at retest has not been established. Only some subscores showed validity against clinical ratings of ataxia (FARS) while all correlated with the SF36 mental and physical component summary scores (Tai et al. 2015b). Responsiveness was poor except for the speech subscale in this 2-year observational study while benchmarks of detectable or important change have not been established. According to current report, the scale has not seen wide application nor translations.

15.4 PROM-Ataxia Only recently, a PRO was specifically developed for use in cerebellar ataxias, designed according to the standards of the PROMIS, “drawing on the knowledge, experience and involvement of patients throughout the process” (Schmahmann et al. 2021). The questionnaire consists of 70 items grouped into the domains of physical symptoms, the domain of activities of daily living and the domain of

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mental health. Selection and weighting were based on perceived relevance, resulting in predominance of physical domain (36 items, 144 maximum score) and less weight to ADL and mental domain (each 17 items and 68 maximum score). Item ratings follow a 5-step Likert scale with zero denoting never/without any difficulty with a time-frame of 2 weeks. Item responses can be summed to above subscores as well as a total score (range: 0–280). The metric properties provided with the original publication support high scale consistency, at least substantial test-retest reliability at 15–30 days retest (almost perfect for ADL subscore) and moderate association with self-reported disability staging. Face validity was confirmed in patient focus groups as perceived importance, relevance to disease and expected responsiveness. A 10-item short form was proposed based on internal consistency measures for the full version. However, both long version and short form await proof of responsiveness from longitudinal assessment and validity testing against clinical ratings in larger and more heterogeneous cohorts.

16 Summary and Perspectives For ataxia research, both generic ataxia rating scales for ataxias of any etiology and disease-specific scales for FRDA and MSA are currently available. Inter-rater reliability is usually reported as high with only minimal rater training. A recent review by the Movement Disorders Society Rating Scales Review Committee (Perez-Lloret et al. 2021) considered all scales as feasible in ataxia populations, but prior validation was often insufficient, specifically with respect to interpretability of score changes. Scale responsiveness is a major concern when selecting outcomes for clinical trials that aim to show effects on disease progression. Responsiveness cannot be validly determined from cross-sectional study. Prior data from longitudinal observation are of eminent importance for sample size calculations and interpretation of score changes. This implies, that feeding existing or future patient registries with scores from standard clinical ataxia ratings would be of great help to set up interventional trials, specifically for rarer ataxia conditions. Collaborative efforts and existing datasets within the ataxia global initiative (ataxia-­global-­initiative.net) are highly relevant in this respect. Still, score changes in placebo arms may differ from score progression expected from natural history studies due to placebo effects. Some data are available from interventional trials that suggest that placebo effects on clinical ataxia ratings do occur but do last only for limited time-frames. In contrast, sensitivity and specificity determined against larger appropriate healthy cohorts are of utmost importance for clinical trials that aim to delay manifestation of disease. As a limitation specifically for early-onset ataxias, results of validation in children showed limitations of clinical ataxia rating scales below the age of 12 years for ICARS, BARS, SARA, and pegboard tests (Lawerman et al. 2017; Brandsma et al. 2014). This may also apply to other quantitative and instrumented motor tests as

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well as to the bedside screen of CCAS. Age effects need further investigation before use in children and available normative datasets should be checked for applicability. The majority of the clinical ataxia rating scales reported herein cover different domains of the cerebellar motor syndrome to different proportions and some also include other (non-ataxia) symptoms (Fig. 2), while CCAS Scale and INAS count deliberately target disease manifestations other than ataxia. Proportions per domain depicted in Fig. 2 was calculated as % of domain scores of total score (which has some uncertainty for PRO, were some items—for example “difficulty playing with children”—cannot be clearly assigned to one or the other domain). Of note, the disease-specific PRO reflects the disease impact also in other non-motor domains, such as sensory symptoms, bowel and bladder functions, sleep and fatigue, cognitive performance, affective disturbance, activities and participation. Researchers should be aware that even reliable and responsive ratings in the motor domain may miss aspects of importance to ataxia patients. The same applies to the upcoming instrumented motor testing that may evolve into motor biomarkers. This supports the recommendation for clinical trials to combine a metrically robust clinical rating or (motor)biomarker/performance measure with measures of patient perception. Patient global impression of change may be used to classify treatment response; however, the relation of such self-report to clinical ratings or motor biomarkers needs far more exploration and cannot be assumed as linear. The perceived importance of change may differ for patients of different disability levels, may change over the individual disease course, and may not be the same for improvement or worsening according to clinical scores. A major concern related to scale responsiveness is the score fluctuations in the short term (day-to-day variability) which hampers a reliable detection of chronic progression, i.e. score point changes. Such variability may only in part be attributed to raters, though interrater reliability is generally reported as high for clinical ratings. Rather, score variability over time may reflect within-subject variability to considerable extent, due to known and unknown factors. Recent methods of remote assessment may yield reliable estimates and determinants of such variability (Grobe-Einsler et al. 2021) to be incorporated in longitudinal data analysis. However, consensus protocols need to be established and standards of analysis from multipoint or continuous data. Also, appropriate quality control of remote data acquisition and applicable algorithms need to be defined for use in clinical studies. In sum, clinical ataxia rating scales contain educated clinical judgment as the major strength that can ensure specificity compared to timed or instrumented motor testing and can improve sensitivity compared to patient-report.

17 Future Directions for Clinical Scales Recently, modifications of existing scales have been proposed driven by some uneasiness with existing scales as well as driven by regulatory decisions. Uneasiness with clinical rating comprises doubts on objectivity, doubtful relevance to patients,

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and assumed inferiority to more reproducible quantitative measures from instrumented analysis. However, such assumptions are not supported by strong evidence and thus there is confusion what exactly a “better clinical rating scale” should look like. The plea for a higher relevance of score changes to those affected markedly contradicts the promotion of quantitative functional biomarkers that may detect even more subtle sub-clinical changes. Others proposed to reconcile different levels of assessment with the implementation of compound outcome assessments, which similarly await definition of appropriate evaluation protocols and interpretation. Applying item-­response theory rather than classical test theory may prove useful also for clinical rating scales to better understand and compare their differential measurement properties and optimal range of application, as has recently been explored for clinical ratings scales in movement disorders (Chae et  al. 2021; Foubert-Samier et al. 2022; Luo et al. 2021). This approach may also be explored to relate results of different instruments or components of a multi-modal assessment to model possibly differential responsiveness to change.

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Scale for Ocular Motor Disorders in Ataxia (SODA): Procedures and Basic Understanding Aasef G. Shaikh, Ji-Soo Kim, Caroline Froment, Yu Jin Koo, Nicolas Dupre, Marios Hadjivassiliou, Jerome Honnorat, Sudhir Kothari, Hiroshi Mitoma, Xavier Rodrigue, Jeremy Schmahmann, Bing-Wen Soong, S. H. Subramony, Michael Strupp, and Mario Manto

Abstract  The cerebellar disorders are evaluated with a number of clinical rating scales. None of these scales emphasize common ocular motor deficits. In instances when an ocular motor aspect of the disease is part of the rating scale, the subscales are limited and do not correlate with appendicular or axial components. This motivated development of a dedicated Scale for Ocular motor Disorders in Ataxia (SODA). The goal of SODA was to objectively measure the burden of ocular motor phenomenology in cerebellar disorder. SODA, like any other rating scale, does not help differentiate the etiology of the disease. This chapter outlines the summary of

A. G. Shaikh (*) Department of Neurology, University Hospitals, Cleveland, OH, USA e-mail: [email protected] J.-S. Kim · Y. J. Koo · X. Rodrigue Department of Neurology, Seoul National University College of Medicine, Bundang, South Korea C. Froment · J. Honnorat Hospices Civils de Lyon, Lyon, France N. Dupre CHU de Québec, Université Laval, Québec, QC, Canada M. Hadjivassiliou Academic Department of Neurosciences Sheffield Teaching hospitals NHS Trust University of Sheffield, Sheffield, UK S. Kothari Department of Neurology, B.J. Medical College, Pune, India H. Mitoma Medical Education Promotion Center, Tokyo Medical University, Tokyo, Japan J. Schmahmann Ataxia Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_11

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SODA and provides a basic understanding of clinical assessment strategies to ­effectively perform ocular motor rating. We also outline mechanistic understanding, why specific aspects of ocular motor examination were included in SODA, what to expect from each of such phenomenologies, and how they are relevant to cerebellar disorders. Keywords  Rating scale · Cerebellum · Eye movements · Saccades · Nystagmus · Gaze

1 Background and Justification Disorders of eye movements are extremely common in patients with cerebellar ataxias. Typical disorders include deficits in gaze-holding, ocular pursuit, rapid gaze shifts (saccades), or vestibulo-ocular reflex (VOR) (Leigh and Zee 2015; Kheradmand and Zee 2011; Feil et al. 2019). The eye movement disorders can be pathognomonic markers of cerebellar impairments, and they carry a potential of an objective outcome measure (see Leigh and Zee 2015). The eye movements are easy to recognize without specialized equipment and they can be monitored with objective instrumented techniques, such as video-oculography. Traditional rating scales for cerebellar ataxias, such as the Scale for Assessment and Rating of Ataxia (SARA), Spinocerebellar Ataxia Functional Index (SCAFI), and International Cooperative Ataxia Rating Scale (ICARS), lack ocular motor objective measures. Modified ICARS and Brief Ataxia Rating Scale (BARS) incorporate a short component of ocular motor assessments. The subscales of ocular motor dysfunction do not correlate with total score or appendicular and axial subscores. Consequently, it is justified to have a dedicated scale to measure ocular motor disorders in ataxias. In order to generalize its application, the scale has to be simplified to facilitate rating by non-specialized examiners; and it has to be short to incorporate in combination with other outcome measures or day-to-day clinical practice. With these goals in B.-W. Soong Taipei Neuroscience Institute, Taipei Medical University, Department of Neurology, Shuang Ho Hospital and Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan S. H. Subramony Department of Neurology, University of Florida, Gainesville, FL, USA M. Strupp Department of Neurology and German Center for Vertigo and Balance Disorders (DSGZ), Ludwig Maximilians University, Munich, Germany M. Manto Service de Neurologie, CHU-Charleroi, Charleroi, Belgium Service de Neurosciences, University of Mons, Mons, Belgium

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mind, a consortium of cerebellar and ocular motor experts was put together; the group generated a simple yet comprehensive Scale for Ocular motor Deficits in Ataxia (SODA). The overarching aim was 1. To establish a measure that can provide the extent of eye movement abnormalities in patients with cerebellar ataxia. 2. To prepare for the drug trials for symptomatic and disease-modifying effects in patients with ataxia secondary to focal/diffuse lesions of the cerebellum. Detailed validation of SODA and its preliminary assessment was published elsewhere (Shaikh et al. 2022). The goal of the current chapter is to outline (1) practical guidelines on how to perform different components of SODA and (2) mechanistic basis for including different components of the ocular motor and vestibular examination in SODA—why specific subgroup of the exam is involved, and what does it account for. Table 1 depicts the summary of SODA. Then we suggest 10 basic “rules” for effective, and accurate eye movement and vestibular examination that would be beneficial for incorporation of SODA in the clinical practice. The subsequent section outlines why the specific aspect of ocular motor examination is selected, how to perform it, and what it contributes to the SODA. Table 1  Scale for ocular motor deficits in ataxia (SODA) Ocular alignment (1 for affirmative response, 0 when absent) Instruction: Examine gaze holding at distant (≥10 feet) target with each eye occluded individually Exotropia Esotropia Skew deviation SUBTOTAL /3 Saccadic intrusions (1 for affirmative response, 0 when absent) Instruction: Examine gaze at straight-ahead, Right, Left, Up, and Down at 45° gaze angle using the examiner’s index as target. The index finger is located about 50 cm from patient’s nose. Each position for 5 seconds. Horizontal saccadic oscillations Vertical saccadic oscillations Square wave jerks SUBTOTAL /3 Jerk nystagmus (here called “nystagmus”) (1 for affirmative response, 0 when absent) Instruction: Examine gaze at straight-ahead, Right, Left, Up, and Down. The index finger of the examiner is used as target at a distance of about 50 cm from the patient’s nose. Each position for 5 seconds. Spontaneous horizontal nystagmus in straight ahead gaze-holding (rebound nystagmus does not qualify) Spontaneous vertical nystagmus in straight ahead gaze-holding Sustained gaze-evoked horizontal nystagmus (no orthogonal [vertical] nystagmus) on right and/or left gaze holding position (continued)

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Table 1 (continued) Rebound nystagmus Gaze-evoked vertical downwards nystagmus on right and/or left gaze Gaze-evoked vertical upwards nystagmus on right and/or left gaze Positional nystagmus (during supine, upright, and right or left ear down testing) SUBTOTAL /7 VOR Cancellation Ask subject to slowly move the head in no-no direction (horizontal) and yes-yes (vertical) and simultaneously ask the subject to align the gaze on target that examiner is moving with the head. While doing it look for corrective saccadic movements (1 if present, 0 if absent) Horizontal VOR Cancellation Vertical VOR Cancellation Ocular pursuit (1 for affirmative response, 0 when absent) Instruction: Ask to follow examiner’s index (or bright target) that is moving slowly in front of the patient at a distance of about 50 cm Horizontal Saccadic pursuit Vertical saccadic pursuit Use higher subtotal value of ocular pursuit or VOR cancellation /2 VOR Instruction: Perform 4 head impulses to the right and 4 head impulses to the left while fixating gaze on the distant (≥10 feet) target (1 for affirmative response, 0 when absent) Saccadic corrections during head impulses to the right OR left Saccadic corrections during head impulses to the right AND left Saccadic corrections during head impulses to the up OR down Saccadic corrections during head impulses to the up AND down SUBTOTAL /4 Saccades, apraxia, and gaze restriction Instruction: Perform 4 horizontal saccades from center (examiners nose) to eccentric bright pointed target (or examiner’s index) on the right side and on left side (2 each). Perform 4 vertical saccades from center (examiners nose) to eccentric bright pointed target down or up (2 each). For each box below, use 1 for affirmative response, 0 when absent. If gaze restriction is present, dysmetria and slowing in in that direction gest maximal point. Dysmetria (Hyper- or Hypo-metria) in horizontal saccades Dysmetria (Hyper- or Hypo-metria) in vertical saccades Slowing of vertical saccade Slowing of horizontal saccades Oculomotor apraxia (look for latency in initiation, NOT multiple gaze shifts to make saccade, i.e., “staircases”) Gaze restriction vertical Gaze restriction horizontal SUBTOTAL /7 TOTAL /26

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2 Ten Rules of Accurate and Effective Examination of Eye Movements and Vestibular Function 2.1 Rule 1: Assuring That the Preliminaries Are Met It is critical to establish that the patient has acceptable visual function after correcting the refractive errors. It is ideal to examine the visual acuity using the pocket acuity card prior to examination and incorporating the findings into SODA.  It is critical to perform fundus examination using ophthalmoscopy. Finally, many neurodegenerative conditions that affect the cerebellum also present with abnormal eyelid function. It is critical to assure that the lids are not closed due to blepharospasms, hemifacial spasm, or lid apraxia.

2.2 Rule 2: Organization in the Clinic, Keeping the Distance Between the Patient and the Visual Target Many ocular motor deficits, particularly those under influence of ocular vergence, are susceptible to the viewing distance. Some types of nystagmus are dampened by convergence, while VOR may have increased gain in presence of closely located visual target. We recommend that the visual target should be 6 to −10 feet away from the patient. We typically follow the organization illustrated in Fig.  1 in the clinic setting. We ask patients to shift gaze from far target to the near target while assessing the depth and vergence dependence of some forms of nystagmus.

2.3 Rule 3: Color of the Visual Target Should Be Bright It is recommended to use bright-colored object while examining the eye movements. Generally, the size of target is that of back of pen cap, about 5 mm in diameter. Red color avoids camouflaging the target and it can be used for red saturation test as well. Fig. 1 Schematic organization of clinic setting. The position of the patient, examiner and the visual target

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2.4 Rule 4: Ocular Alignment Ocular alignment is an important aspect of SODA.  We recommend translucent occluder for this purpose. The phenomenon of interest is the involuntary disconjugate deviation of the covered eye, but as the visual fixation is allowed, the involuntarily turned eye re-fixates on the object of interest. The translucent occluder allows visualization of the covered eyes by the examiner, while preventing patient’s vision through the occluder.

2.5 Rule 5: Age-Related Changes and Effects of Medications Cerebellar disorders are not uncommon in elderly people. Age, even in the absence of central or peripheral pathologies, can lead to atrophy, fibrosis, and restricted movements of the eyes due to changes in the orbit. Therefore, it is not uncommon for the elderly persons to have limited upward eye movements or convergence. It is essential to consider the patients’ home medications while interpreting the ocular motor examination. A number of medications cause involuntary eye movements, typically antiepileptics lead to nystagmus. Some types of pharmacotherapies, especially benzodiazepines or narcotics, affect the saccade velocity.

2.6 Rule 6: Stabilize the Patient’s Head It is essential to stabilize patients’ heads while performing the ocular motor examination. This is particularly critical in those with hyperkinetic movement disorders, as often seen in patients with cerebellar impairments. Even small head movements, primarily generated at the neck or transmitted from the limb or trunk, can trigger VOR. If these movements are associated with vestibular hypofunction, then it gives an impression of nystagmus, that is, “pseudonystagmus.” Adequate head stabilization will rule out pseudonystagmus.

2.7 Rule 7: Pay Attention to the Eyelids Subtle vertical eye oscillations can be diagnosed by observing the lid movements. The physiological rationale is that the vertical eye movements are yoked with the action of the levator palpebrae; every time we look up the eyelids contract and go up; and vice versa. We suggest focusing on the eyelashes or eyelids to look for subtle vertical eye oscillations of upbeat or downbeat nystagmus.

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2.8 Rule 8: Use an Ophthalmoscope if Needed Visual deficits, such as shimmering or blurred vision, can be seen with ocular flutter or micro-opsoclonus, collectively called saccadic oscillations. These deficits can also manifest as “dizziness.” Examination with bare eye may not reveal microflutter or microopsoclonus, in which case it is essential to examine the eye movements with ophthalmoscope. We recommend focusing on the optic disc and the rotations of the blood vessels around the optic disc. It is also important to keep in mind that under ophthalmoscopy the eye movement direction will be switched. The reason is that the eyeballs rotate on the axis that passes from the middle of globe. The optic disc is on the other side, so the downward movement of the iris or the front of the eye, for example, is equivalent to the upward movement of the optic disc.

2.9 Rule 9: Look at the Bridge of the Nose It is critical to notice subtle disconjugacy between the two eyes. In order to note the subtlety such as mild seesaw nystagmus, internuclear ophthalmoplegia, or dynamic disconjugacy during saccades and pursuits, we suggest looking at the bridge of the patient’s nose while focusing on two eyes simultaneously.

2.10 Rule 10: Head Impulses Should Be Brief but Fast Head impulses are important part of an examination of the VOR, an important component of SODA. The head impulses should be done in all three canal planes; horizontal, right anterior left posterior, and left anterior right posterior. In each plane, the head impulses should be fast but with brief excursions, less than 5°. The large excursions of the head impulses will render patients at the risk for developing neck pain or even worse sequels such as dissection.

3 Organizational Components of SODA The goal of SODA is to identify an ocular motor abnormality in patients with cerebellar ataxia, while assuring it is simple enough to be utilized by nonexpert operators. We could reach this goal (Shaikh et al. 2022). This chapter will further educate the interested raters to further learn effective ways to perform SODA. The current section focuses on various aspects of SODA, how they can be performed and interpreted, and why they were included in SODA.  The readers may be interested in mechanistic underpinning of various ocular motor abnormalities in the cerebellar

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disease. While critical to have this knowledge, its inclusion here in this chapter will dilute the focus on SODA. Such readers are referred to the reference text, such as The Neurology of Eye Movements (Leigh and Zee 2015).

3.1 Ocular Alignment Ocular misalignments such as exotropia, esotropia, and skew deviation are not uncommon in cerebellar disorders (Ghasia et al. 2016; Goldstein and Cogan 1961; Harris et al. 1993; Hufner et al. 2015; Khan et al. 2008; Kono et al. 2002; Rabiah et al. 1997; Wong et al. 2015). It is critical because typically any clinically identifiable ocular misalignment leads to diplopia. Ocular misalignment is often seen in people with multiple system atrophy, certain forms of spinocerebellar ataxia (e.g., SCA3), ataxia-telangiectasia, or even cerebellar strokes. Esotropia, or exotropia, or skew deviation each was assigned 1 point in SODA.

3.2 Fixation Deficits (Saccadic Intrusions) Two types of saccadic intrusions are noteworthy—saccadic oscillations and square wave jerks. Saccadic oscillations are back-to-back saccades without intersaccadic intervals. When unidirectional they are called ocular flutter, while multidimensional saccadic oscillations are called opsoclonus. Flutter and opsoclonus are not uncommon in cerebellar syndromes due to autoimmune or degenerative process (Ghasia et al. 2016; Desai and Mitchell 2012; Ellenberger Jr. et al. 1968; Ellenberger Jr. and Netsky 1970; Helmchen et al. 2003; Hersh et al. 1994; Jen et al. 2012; Optican and Pretegiani 2017; Ross and Zeman 1967; Shaikh and Wilmot 2016; Theeranaew et al. 2021; Tuchman et al. 1989; Wong et al. 2001; Wray et al. 2011). They are commonly seen in syndrome of anti-GAD antibody, SCA3, opsoclonus-myoclonus-­ ataxia, and ataxia-telangiectasia (Shaikh et  al. 2009; Tang and Shaikh 2019). Uniplanar fine oscillations, such as ocular flutter, are generally mild and less chaotic compared to multiplanar coarse opsoclonus. Therefore in SODA, horizontal saccadic oscillations were given 1 point, and when vertical oscillations are also present they receive another 1 point. The square wave jerks are other form of saccadic intrusions. They are frequently seen with psychiatric conditions, such as schizophrenia. Typically, they do not affect visual function, unless when they are excessive in frequency or present as entrained back-to-back square waves with increased amplitude. The classic example of cerebellar deficit causing symptomatic square waves includes spinocerebellar ataxia and saccadic intrusions (Rosini et  al. 2013; Serra et al. 2008). Square waves are always horizontal; it is extremely rare to have vertical square waves. When present square waves are given 1 point on SODA.

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3.3 Jerk Nystagmus Jerk nystagmus, here called “nystagmus,” features slow drifts in the position of the eyes followed by rapid (corrective) movement (i.e., the quick phase). The nystagmus in cerebellar disorders is of many types, for example, gaze-evoked nystagmus, rebound nystagmus, downbeat nystagmus, and positional nystagmus, and upbeat nystagmus (Theeranaew et  al. 2021; Wray et  al. 2011; Baloh and Yee 1989; Benjamin et al. 1986; Gilman et al. 1977; Higashi-Shingai et al. 2012; Kanaya et al. 1994; Kato et al. 1985; Schmidt 2011; Shin et al. 2010; Baloh and Spooner 1981; Bertholon et al. 2003; Cho et al. 2017; Choi et al. 2012, 2014; Jeong et al. 2011; Kim et al. 2013; Moon et al. 2009; Norre and Puls 1981; Sakata et al. 1987; Yabe et al. 2003). Nystagmus leads to significant impairment and is a hallmark of cerebellar dysfunction. Therefore, each form of nystagmus was considered an individual item in SODA.  In each condition, when nystagmus is present, it scores 1 point; maximum score reaches 7. Subsequent section outlines each type of nystagmus, its significant in relevance to cerebellar disorders, and SODA. 3.3.1 Gaze-Evoked Nystagmus The gaze-evoked nystagmus is the most common type of nystagmus. The eyes, when in eccentric position, drift towards the central orientation. The drift is followed by a quick phase. As a result right-ward gaze holding leads to right beating nystagmus; and left-ward gaze has left beating nystagmus. Although nystagmus is named according to the direction of the quick phase, that is, the “beat” direction, the pathognomonic aspect of the nystagmus is slow drift. The drifts are result of impaired integration of the saccade velocity command that is meant to convert it into steady state position under cerebellar feedback. Impaired cerebellar feedback in form of the cerebellar disease leads to abnormal integration and subsequently the nystagmus. As a result, the eye velocity during drift increases as the desired eccentric eye position shifts farther away from the null. As the eyes change orientation from one side of the null to the other, the drift direction also reverses. The gaze-­ evoked nystagmus is not only horizontal, but it can be seen in vertical direction; upbeat nystagmus in upward eye position while downbeat in downward direction. In some cases, gaze-evoked nystagmus is seen in both horizontal and vertical direction, hence eccentric gaze holding leads to oblique, or side pocket nystagmus. During central gaze, the eyes are relatively steady, but after sustained eccentric orientation, the central gaze has drifts in the direction opposite of the eccentric gaze drift. Latter phenomenon is called “rebound nystagmus.” For example, after leftward gaze holding that triggers left beating gaze-evoked nystagmus the eyes in central orientation will have right beat rebound nystagmus. Figure 2 depicts a schematic depicting the trend of gaze-evoked nystagmus. The central position is depicted with

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Fig. 2 Schematic depicting the ocular trajectory and intensity during gaze-evoked nystagmus. The central position with circle is when the eye velocity is null. The slow-­phase eye velocity depicting drifts increase with eccentric eye in orbit orientations. Farther away orientation depicts larger slow-phase eye velocity. The size of arrow depicts larger slow-phase eye velocity. The direction of the arrow depicts the direction of eye trajectory

a circle where the eyes are stable. The arrow size in the figure depicts the intensity (the slow phase velocity) of the nystagmus, while the direction of the arrow depicts the direction of quick phase. Presence of gaze-evoked nystagmus will be scored 1 in SODA scale. 3.3.2 Downbeat Nystagmus The downbeat nystagmus features upward drifts in the eye position followed by downward quick phase. It is the second most common form of nystagmus. Often obvious during clinical examination, in some instances one has to carefully observe the movements of eyelids to recognized downbeat nystagmus in its subtle forms. Generally, in downbeat nystagmus the eyes are relatively steady in upward gaze, but it has increased velocities of the drifts as the eye in orbit position shifts further in the downgaze. Occasionally the eye in orbit position dependence of the slow phase velocity reverses, the eye velocity is more in upgaze, and the eyes are stable in downgaze. Downbeat nystagmus is often seen with gaze-evoked nystagmus, that is, in eccentric horizontal gaze there is downbeat with left and right beat. Downbeat nystagmus is sometimes seen with headshaking, called “perverted” head shaking nystagmus. While spontaneous forms of nystagmus are part of SODA; perverted head shaking nystagmus is not considered a putative consideration in SODA. The typical trend of downbeat nystagmus is depicted in the Fig. 3, where stable eye position is illustrated with circles, while the arrow size depicts the intensity (the slow phase velocity) of the nystagmus. The direction of the arrow illustrates the direction of the quick phase. Presence of downbeat nystagmus in primary sitting position will be scored 1 in SODA scale.

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Fig. 3 Schematic depicting the ocular trajectory and intensity during downbeat nystagmus. In typical cases of downbeat nystagmus the up-­gaze is steady as depicted with circle. The slow-phase eye velocity depicting drifts increase with downward eye in orbit orientations. Increasingly downward orientation depicts larger slow-­phase eye velocity. The size of arrow depicts larger slow-­phase eye velocity. The direction of the arrow depicts the direction of eye trajectory

3.3.3 Upbeat Nystagmus Upbeat nystagmus is also seen in cerebellar disorders, but it is much rare compared to vertical downbeat nystagmus. Typically during upbeat nystagmus, the eyes are relatively stable in downgaze, but their slow phase velocity increases with upgaze. The drifts are downwards and beats are upwards; the eyes are stable in downgaze. The upbeat nystagmus generally suggests brainstem pathophysiology, but is also seen in with the cerebellar disorders. The upbeat nystagmus can be present as a part of gaze-evoked nystagmus when the eyes are in eccentric upgaze. Figure 4 depicts the summary of upbeat nystagmus. Here the circles are stable eye position, while the arrow size illustrate the nystagmus slow phase velocity. The quick phase direction is illustrated with the arrow direction. Presence of upbeat nystagmus during primary position will be scored 1 in SODA scale. 3.3.4 Positional Nystagmus Positional nystagmus is not uncommon in cerebellar disorders, particularly those affecting the cerebellar nodulus. The nystagmus slow phase direction changes according to the head position, and the trend is determined by the cerebellar nodulus and ventral uvula. Often positional nystagmus can be mixed with benign paroxysmal positional vertigo, but latter has more stereotypic course and would not have accompanying movement disorders. SODA will be scored 1 if positional nystagmus is present, in any one or more head orientation, regardless of its etiology.

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Fig. 4 Schematic illustration of the ocular trajectory and intensity during upbeat nystagmus. The downward position with circle is when the eye velocity is null. The slow-­phase eye velocity depicting drifts increase with increasing upward eye-in-orbit orientations. The size of arrow depicts larger slow-phase eye velocity. The direction of the arrow depicts the direction of eye trajectory

3.4 VOR The VOR is a critical aspect of ocular motor and vestibular examination. It is defined as a compensatory physiological eye movement in response to head movements. But it has to move at the same velocity as that of the head. It is a fundamental requirement of the VOR that the eye velocity and direction of gaze shift are precisely matched with the head velocity. Mismatch in this matric can lead to impaired visual function while locomotion. There are three ways to measure the VOR. One is the head impulse test where the head rapidly moved by the examiner in three individual canal planes—horizontal VOR, right anterior left posterior, and right posterior left anterior—vertical VOR. Another strategy includes sinusoidal oscillations of the head looking for directional and gaze disparity in VOR. Finally, the head-­shaking test is a sensitive way to examine the VOR. In head-shaking nystagmus the gaze is examined in post head-shaking phase, and normally it should be stable. For simplicity, SODA only outlines most obvious test of VOR function, that is horizontal and vertical head impulse testing. The VOR hypofunction is typically considered deficit affecting the peripheral end-organs; the cerebellar dysfunction can also lead to impaired matrix of the VOR in both velocity and directional domains (i.e., perverted VOR). The impairment is secondary to inability for cerebellum to have error correction mechanism.

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3.5 Saccades The saccades are rapid eye movements made to shift gaze from one object to the other. In a given day, the humans make thousands of voluntary or involuntary saccades. Saccade size, speed, and promptness (latency) are critical to their examination. The size can be classified in larger or smaller than desired, that is, hypermetria and hypometria. Direction is measured by curved saccades, sometimes called “round the houses sign.” In contrast, the velocity is much pathognomonic and depicting much worse from of cerebellar or/and brainstem disorder. Abnormal matrix of saccades depicts impaired learning and error correction mechanism that is hallmark of new cerebellar disorders.

3.6 Pursuits and VOR Cancellation The eyes smoothly follow slowly moving target. Such eye movements, called pursuit, are examined by asking patients to follow slowly moving object in the clinic, or often the examiner’s finger. Interruption in the pursuit eye movements suggests cerebellar dysfunction. In many instances of cerebellar disorders, the gaze-evoked nystagmus is present during the test of pursuit function. Latter interferes with adequate assessment of pursuit; hence, VOR-cancellation is practiced, where the moving target shifts with the head, and subject has to “cancel” the VOR to keep the eyes steady to view the target. The “cancellation” of VOR utilizes the pursuit pathway, hence impaired pursuit would lead to abnormal cancellation of VOR. SODA views pursuit and VOR cancellation as same phenomenology, and only accounts for higher of the two scores. Acknowledgments  AS was supported by philanthropic support to the Department of Neurology (Penni and Stephen Weinberg Chair in Brain Health) and the Department of Veterans Affairs Merit Review Grant (I01CX002086). Conflict of Interest (CoI) Statement  The authors have no conflict of interest pertinent to this work.

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Cerebellar Learning in the Prism Adaptation Task Takeru Honda and Hidehiro Mizusawa

Abstract  Compared with healthy subjects, patients with cerebellar degeneration find it difficult to adaptively change the movement of throwing a dart toward a virtual target image seen through a prism to that toward the actual target (prism adaptation task). This suggests that the cerebellum is related to adaptive learning. We developed a device with which anyone can perform the prism adaptation task and determine the adaptability index (AI) to estimate the capability for cerebellar learning. On the basis of basic science, it is hypothesized that the cerebellum learns internal models. In the prism adaptation task, the patients find it difficult to update either (i) the inverse model or (ii) both the forward and inverse models. Thus, the prism adaptation task can be used to estimate the capability for cerebellar learning by measuring AI. It can also be used to estimate in detail what the cerebellum learns: the forward or inverse model. Keywords  Prism adaptation task · Cerebellum · Cerebellar degeneration · Adaptability index (AI) · Internal model · Forward model · Inverse model · Adaptive learning · Cerebellar learning · Long-term depression (LTD)

1 Clinical Practice Many conditions, such as neoplasm, trauma, congenital malformation, inflammation (including infection), immune-mediated conditions, vascular disorders, intoxication, metabolic disorders, and degeneration (Manto and Pandolfo 2002), affect T. Honda Tokyo Medical and Dental University, Tokyo, Japan e-mail: [email protected] H. Mizusawa (*) National Center of Neurology and Psychiatry, Tokyo, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 B.-w. Soong et al. (eds.), Trials for Cerebellar Ataxias, Contemporary Clinical Neuroscience, https://doi.org/10.1007/978-3-031-24345-5_12

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the cerebellum. Thus, for such conditions, we should evaluate the cerebellar function to assess disease severity and etiology. In clinical practice, cerebellar signs have been detected using diagnostic methods devised by Joseph Babinski (Babinski 1899) and Gordon Holmes (Holmes 1939). Clinical scales such as the International Ataxia Rating Scale (ICARS) (Trouillas et al. 1997) and the Scale for the Assessment and Rating of Ataxia (SARA) (Schmitz-­ Hubsch et al. 2006) have been developed and used in routine medical examinations. Neurodegeneration is very difficult to diagnose, evaluate, and treat. For example, the SARA score is a subjective measure and depends on the experience and skill of the examiner, and its change is very subtle, only approximately one point per year in spinocerebellar ataxia type 6 (SCA 6) (Jacobi et al. 2011; Ashizawa et al. 2013; Yasui et al. 2014; Moriarty et al. 2016). Progression seems very slow in many SCA cases. A change of one point in SARA could be attributed to the placebo effect (Nishizawa et  al. 2020). Therefore, a quantitative method is required to evaluate cerebellar function. This is very important for clinical trials of rare neurodegenerative diseases of the cerebellum (Manto and Pandolfo 2002). It is considered that the factor underlying cerebellar symptoms is incoordination or coordination disorder, in which the synkinesis of muscles for performing various combinations of movements is impaired (Babinski 1899; Holmes 1939). With the latest technological development, the depth sensor Kinect v2 (from Microsoft Co.), which can measure the distance of a healthy subject’s body from the sensor with infrared rays, has become available for the objective measurement of motor function of humans (Shotton et al. 2011). For example, in the finger-to-nose test, movements of not only the fingers but also the elbows and trunk can be objectively measured and compared between patients with cerebellar disease and healthy subjects (Honda et al. 2020). It is expected that such a measuring instrument will clarify the effect of coordination disorder on movement. Furthermore, it is understood that the factors underlying cerebellar symptoms are predictive movement disorder, in which an appropriate movement trajectory cannot be predicted in advance, and adaptation disorder, in which accurate movement adaptation to the surrounding environment is impaired. In the field of neurophysiology, it has been hypothesized that the cerebellum has motor learning function through synaptic plasticity (i.e., Ito 1984; Nagao 2021). There reported were some disorders of the learning function of the cerebellum as an underlying factor of the predictive movement disorder and adjustment disorder.

2 Prism Adaptation Task A prism adaptation task has been performed by Tom Thach and his colleagues (Martin et al. 1996). When healthy subjects wore prism glasses, their field of vision was biased and they threw darts toward the virtual image of the target, making it impossible to achieve correct movement. However, by repeating dart throwing, adaptive learning occurred in response to the change in visual information, and they

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could finally hit the actual target accurately. In a patient with cerebellar degeneration, even after throwing the dart repeatedly, no such learning occurs, so the patient continues to throw toward the virtual image of the target seen through the prism lens. Therefore, it is considered that the cerebellum is involved in prism adaptation.

3 Adaptability Index (AI) In dart throwing, we must prepare a large examination room. Subjects, including patients, require some basic skills, that is, there are good and bad dart throwers. Therefore, we developed a system comprising a hand-reaching task with a touch panel so that the test can be conducted in outpatient clinics (Hashimoto et al. 2015) (Fig. 1). The system hardware consists of a personal computer (task control, data sampling and analysis), a touch screen, a pair of goggles that can fit a prism, and an ear sensor. The goggle is outfitted with an electrically controlled shutter, which opens upon applying a pulse-on command voltage (100 V) and closes on applying a pulse-off voltage. A target appears randomly at one of eight positions on the screen as the subject touches the sensor (Fig. 1a). The goggles are connected to a sensor attached to the ear and the shutter opens when the subject touches, with the dominant hand, the sensor on the ear, or the touchscreen in front where the target is shown. As a result, just as the dart cannot be controlled after it is thrown, when the hand is released from the ear sensor, the field of vision is closed by the shutter on the goggles, and the movement of the arm follows the moment of inertia. The actual procedure is shown in Fig.  2a. We prepared the following three sessions. 1 . 50 trials with normal vision (BASELINE session) 2. 100 trials wearing prism glasses shifting the visual field 25°rightward (PRISM session) 3. 50 trials without the prism glasses (REMOVAL session) In order to measure a large error of reaching, it might be advisable to use a prism lens that largely deviates the field of view. However, when we used the prism lens with a refraction angle larger than 25°, the touch positions were outside the 23-inch monitor of the touch panel. A healthy subject took around 20 minutes to complete the three sessions. In healthy subjects, the baseline phase showed almost no deviation; wearing the prism lens resulted in a steep deviation but soon returned to the baseline after repeated trials owing to motor learning or prism adaptation (Fig. 2a). Finally, removal of the prism caused a deviation to the opposite side because the adaptation was complete and again quickly returned to the baseline owing to the second adaptation. In a SCA31 patient, the pattern was not normal even in the baseline phase, which is indicative of dysmetria (Fig. 2b). We have developed AI values quantified the adaptive learning function which are collected as stable data by using touch sensor technology to change the prism

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A Touch Screen

Sensor on Subject’s Earlobe Server Computer for Task Conrtrol, Data Sampling and Analysis Client Computer for Task Execution

B

VISION ON

VISION OFF

REACTION TIME

MOVEMENT DURATION

START TARGET ON

RELEASE

VISION ON EXPOSURE

100ms TOUCH

RETURN TIME

TARGET OFF

END

Fig. 1  Scheme for prism adaptation of hand-reaching. These figures were modified from those in our previous report (Hashimoto et al. 2015). (a) The system used in experiments consists of a sensor on the participant’s right earlobe, goggles equipped with an electrically controlled shutter with a plastic or Fresnel prism plate, a touchscreen, and two computers. (b) Time sequence of single trial shown from left to right. Each trial starts from the time the subject touches the sensor on the right earlobe with the index finger. As soon as the subject releases their index finger from the sensor, vision is blocked by the shutter (MOVEMENT TIME). Immediately after touching the touchscreen (TOUCH), the goggles become transparent, and the subject can see how their index finger deviated from/hit the target for 100  ms (EXPOSURE). Subsequently, the target disappears (TARGET OFF) and the subject returns their index finger to the original position in preparation for the next trial

adaptation task from throwing darts to the reaching movement of the hand (Hashimoto et al. 2015). This has made it possible to objectively measure motor learning functions involving the cerebellum. From the data of healthy subjects, each trial was classified into one of two outcomes, “successful” and “unsuccessful.” “Success” here means that there is an error of 25 mm or less from the center of the target (Fig. 2a). We define a, b, and c as the number of successes in the final 10 trials “with prism” (acquisition), the number of successes during the 5 trials of starting “without prism” (retention), and the number of successes in the final 10 trials

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Fig. 2  Adaptation curves for a healthy subject (a) and a patient with SCA31 (b). The ordinate shows the finger-touch error represented by the distance (mm) from the target to the touch point. Positive values indicate rightward shifts and negative values indicate leftward shifts. The abscissa shows the trial numbers. Best-fitted exponential curves are overlaid on the raw data

“without prism” (extinction), respectively. From these values, the AI that takes a value of 0 to 1 was obtained using the formula AI = a × (1 − b) × c (Fig. 2). Whereas a healthy subject had AI = 1.000 × (1 − 0.000) × 1.000 = 1.000 (Fig. 2a), a SCA31 patient had AI = 0.300 × (1 − 0.800) × 0.900 = 0.054 (Fig. 2b). Adding the type of healthy subjects (Fig.  3a, AI  =  1.000), we categorized the patients with cerebellar degeneration in accordance with their type of behavior in the prism adaptation task. One group showed low values of a, suggesting an

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abnormality in memory acquisition (Fig. 3b, AI = 0.120). The second group showed high a and c values but low b values, suggesting an abnormality in memory retention (Fig. 3c, AI = 0.000). The third group showed high a and b values but low c values, indicating an abnormality in memory extinction (Fig. 3d, AI = 0.144). The actual AI and age of each patient are shown in Fig.  3e (Hashimoto et  al. 2015). Older subjects had significantly lower AI than younger subjects (Fig.  3f). When the AI was compared between patients with cerebellar degeneration and healthy subjects, there was very little overlap (Fig. 3e), suggesting that AI could be used to distinguish patients with cerebellar degeneration from healthy subjects. Indeed, AI below 0.68 had a sensitivity of 98.4% and a specificity of 100% for healthy subjects and patients with cerebellar degeneration (Hashimoto et al. 2015). AI values of patients with high SARA scores (Fig. 3g) or long time in the 9-hole peg test (9HPT) (Fig. 3h), are lower than the cut-off level. As expected, AI progressively decreased with the duration of the disease (Fig. 3i). Until now, there has been no biomarker that can be applied to early-stage spinocerebellar degeneration and it is difficult to judge the therapeutic effect on the basis of objective evaluation indexes. Therefore, no fundamental treatment method has been developed for various types of spinocerebellar degeneration. However, it is important to detect minute changes sensitively in clinical practice, and it is expected that objective evaluation indicators such as AI will lead to early detection of such degeneration. As an example, together with this AI, it will be possible to investigate the relationship between motor learning function and the location of cerebellar atrophy caused by a disease from cerebellar volume measurement by MRI (Voxel-Based Morphometry). We found that AI is significantly correlated with cerebellar hemispheric atrophy in the right lobule VI and the left Crus I in the cerebellum, suggesting that these areas are involved in adaptive learning in the prism adaptation task (Bando et al. 2019). It is expected that further knowledge will be accumulated and AI will be utilized for early diagnosis and clinical trials. Moreover, in order to relationship between learning function and the ataxia severity, we introduce findings in basic science for the cerebellum. Fig. 3  Adaptation curves for different subjects in healthy and patient groups. These figures were modified from those in our previous report (Hashimoto et al. 2015). (a–d) Adaptation curves for a healthy subject (a), patients with SCA6 (b, c), and a patient with SCA31 (d). The ordinate shows the finger-touch error represented by the distance (mm) from the target to the touch point. Positive values indicate rightward shifts and negative values indicate leftward shifts. The abscissa shows the trial numbers. Best-fitted exponential curves are overlaid on the raw data. Whereas a normal subject shows typical adaptation (a), patients with cerebellar diseases show three different patterns of impaired adaptation (b–d). (e) Distribution of AI values and ages for all the subjects analyzed. AI tended to decrease and showed a widespread distribution in a group of the elderly healthy subjects (HE) 70 years old and over. Cerebellar patients (CN who are under 70 years old and CE who are 70 years old and over) showed lower AI values than the age-matched healthy subjects (HN and HE). † indicates four pure parkinsonian MSA patients without clinical cerebellar signs. (f) Comparison of AI among the HN, HE, CN, and CE groups. In all panels, red circles and columns represent HN; magenta, HE; blue, CN; and green, CE. **p